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14908:{\displaystyle {\begin{aligned}&\propto \prod _{i\neq k}\Gamma \left(n_{m,(\cdot )}^{i,-(m,n)}+\alpha _{i}\right)\prod _{i\neq k}{\frac {\Gamma \left(n_{(\cdot ),v}^{i,-(m,n)}+\beta _{v}\right)}{\Gamma \left(\sum _{r=1}^{V}n_{(\cdot ),r}^{i,-(m,n)}+\beta _{r}\right)}}\Gamma \left(n_{m,(\cdot )}^{k,-(m,n)}+\alpha _{k}+1\right){\frac {\Gamma \left(n_{(\cdot ),v}^{k,-(m,n)}+\beta _{v}+1\right)}{\Gamma \left(\sum _{r=1}^{V}n_{(\cdot ),r}^{k,-(m,n)}+\beta _{r}+1\right)}}\\&=\prod _{i\neq k}\Gamma \left(n_{m,(\cdot )}^{i,-(m,n)}+\alpha _{i}\right)\prod _{i\neq k}{\frac {\Gamma \left(n_{(\cdot ),v}^{i,-(m,n)}+\beta _{v}\right)}{\Gamma \left(\sum _{r=1}^{V}n_{(\cdot ),r}^{i,-(m,n)}+\beta _{r}\right)}}\Gamma \left(n_{m,(\cdot )}^{k,-(m,n)}+\alpha _{k}\right){\frac {\Gamma \left(n_{(\cdot ),v}^{k,-(m,n)}+\beta _{v}\right)}{\Gamma \left(\sum _{r=1}^{V}n_{(\cdot ),r}^{k,-(m,n)}+\beta _{r}\right)}}\left(n_{m,(\cdot )}^{k,-(m,n)}+\alpha _{k}\right){\frac {n_{(\cdot ),v}^{k,-(m,n)}+\beta _{v}}{\sum _{r=1}^{V}n_{(\cdot ),r}^{k,-(m,n)}+\beta _{r}}}\\&=\prod _{i}\Gamma \left(n_{m,(\cdot )}^{i,-(m,n)}+\alpha _{i}\right)\prod _{i}{\frac {\Gamma \left(n_{(\cdot ),v}^{i,-(m,n)}+\beta _{v}\right)}{\Gamma \left(\sum _{r=1}^{V}n_{(\cdot ),r}^{i,-(m,n)}+\beta _{r}\right)}}\left(n_{m,(\cdot )}^{k,-(m,n)}+\alpha _{k}\right){\frac {n_{(\cdot ),v}^{k,-(m,n)}+\beta _{v}}{\sum _{r=1}^{V}n_{(\cdot ),r}^{k,-(m,n)}+\beta _{r}}}\\&\propto \left(n_{m,(\cdot )}^{k,-(m,n)}+\alpha _{k}\right){\frac {n_{(\cdot ),v}^{k,-(m,n)}+\beta _{v}}{\sum _{r=1}^{V}n_{(\cdot ),r}^{k,-(m,n)}+\beta _{r}}}\end{aligned}}}
11094:
12658:{\displaystyle {\begin{aligned}P(&Z_{(m,n)}=v\mid {\boldsymbol {Z_{-(m,n)}}},{\boldsymbol {W}};\alpha ,\beta )\\&\propto P(Z_{(m,n)}=v,{\boldsymbol {Z_{-(m,n)}}},{\boldsymbol {W}};\alpha ,\beta )\\&=\left({\frac {\Gamma \left(\sum _{i=1}^{K}\alpha _{i}\right)}{\prod _{i=1}^{K}\Gamma (\alpha _{i})}}\right)^{M}\prod _{j\neq m}{\frac {\prod _{i=1}^{K}\Gamma \left(n_{j,(\cdot )}^{i}+\alpha _{i}\right)}{\Gamma \left(\sum _{i=1}^{K}n_{j,(\cdot )}^{i}+\alpha _{i}\right)}}\left({\frac {\Gamma \left(\sum _{r=1}^{V}\beta _{r}\right)}{\prod _{r=1}^{V}\Gamma (\beta _{r})}}\right)^{K}\prod _{i=1}^{K}\prod _{r\neq v}\Gamma \left(n_{(\cdot ),r}^{i}+\beta _{r}\right){\frac {\prod _{i=1}^{K}\Gamma \left(n_{m,(\cdot )}^{i}+\alpha _{i}\right)}{\Gamma \left(\sum _{i=1}^{K}n_{m,(\cdot )}^{i}+\alpha _{i}\right)}}\prod _{i=1}^{K}{\frac {\Gamma \left(n_{(\cdot ),v}^{i}+\beta _{v}\right)}{\Gamma \left(\sum _{r=1}^{V}n_{(\cdot ),r}^{i}+\beta _{r}\right)}}\\&\propto {\frac {\prod _{i=1}^{K}\Gamma \left(n_{m,(\cdot )}^{i}+\alpha _{i}\right)}{\Gamma \left(\sum _{i=1}^{K}n_{m,(\cdot )}^{i}+\alpha _{i}\right)}}\prod _{i=1}^{K}{\frac {\Gamma \left(n_{(\cdot ),v}^{i}+\beta _{v}\right)}{\Gamma \left(\sum _{r=1}^{V}n_{(\cdot ),r}^{i}+\beta _{r}\right)}}\\&\propto \prod _{i=1}^{K}\Gamma \left(n_{m,(\cdot )}^{i}+\alpha _{i}\right)\prod _{i=1}^{K}{\frac {\Gamma \left(n_{(\cdot ),v}^{i}+\beta _{v}\right)}{\Gamma \left(\sum _{r=1}^{V}n_{(\cdot ),r}^{i}+\beta _{r}\right)}}.\end{aligned}}}
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9606:{\displaystyle {\begin{aligned}&\int _{\boldsymbol {\varphi }}\prod _{i=1}^{K}P(\varphi _{i};\beta )\prod _{j=1}^{M}\prod _{t=1}^{N}P(W_{j,t}\mid \varphi _{Z_{j,t}})\,d{\boldsymbol {\varphi }}\\={}&\prod _{i=1}^{K}\int _{\varphi _{i}}P(\varphi _{i};\beta )\prod _{j=1}^{M}\prod _{t=1}^{N}P(W_{j,t}\mid \varphi _{Z_{j,t}})\,d\varphi _{i}\\={}&\prod _{i=1}^{K}\int _{\varphi _{i}}{\frac {\Gamma \left(\sum _{r=1}^{V}\beta _{r}\right)}{\prod _{r=1}^{V}\Gamma (\beta _{r})}}\prod _{r=1}^{V}\varphi _{i,r}^{\beta _{r}-1}\prod _{r=1}^{V}\varphi _{i,r}^{n_{(\cdot ),r}^{i}}\,d\varphi _{i}\\={}&\prod _{i=1}^{K}\int _{\varphi _{i}}{\frac {\Gamma \left(\sum _{r=1}^{V}\beta _{r}\right)}{\prod _{r=1}^{V}\Gamma (\beta _{r})}}\prod _{r=1}^{V}\varphi _{i,r}^{n_{(\cdot ),r}^{i}+\beta _{r}-1}\,d\varphi _{i}\\={}&\prod _{i=1}^{K}{\frac {\Gamma \left(\sum _{r=1}^{V}\beta _{r}\right)}{\prod _{r=1}^{V}\Gamma (\beta _{r})}}{\frac {\prod _{r=1}^{V}\Gamma (n_{(\cdot ),r}^{i}+\beta _{r})}{\Gamma \left(\sum _{r=1}^{V}n_{(\cdot ),r}^{i}+\beta _{r}\right)}}.\end{aligned}}}
8364:{\displaystyle {\begin{aligned}&\int _{\theta _{j}}P(\theta _{j};\alpha )\prod _{t=1}^{N}P(Z_{j,t}\mid \theta _{j})\,d\theta _{j}=\int _{\theta _{j}}{\frac {\Gamma \left(\sum _{i=1}^{K}\alpha _{i}\right)}{\prod _{i=1}^{K}\Gamma (\alpha _{i})}}\prod _{i=1}^{K}\theta _{j,i}^{n_{j,(\cdot )}^{i}+\alpha _{i}-1}\,d\theta _{j}\\={}&{\frac {\Gamma \left(\sum _{i=1}^{K}\alpha _{i}\right)}{\prod _{i=1}^{K}\Gamma (\alpha _{i})}}{\frac {\prod _{i=1}^{K}\Gamma (n_{j,(\cdot )}^{i}+\alpha _{i})}{\Gamma \left(\sum _{i=1}^{K}n_{j,(\cdot )}^{i}+\alpha _{i}\right)}}\int _{\theta _{j}}{\frac {\Gamma \left(\sum _{i=1}^{K}n_{j,(\cdot )}^{i}+\alpha _{i}\right)}{\prod _{i=1}^{K}\Gamma (n_{j,(\cdot )}^{i}+\alpha _{i})}}\prod _{i=1}^{K}\theta _{j,i}^{n_{j,(\cdot )}^{i}+\alpha _{i}-1}\,d\theta _{j}\\={}&{\frac {\Gamma \left(\sum _{i=1}^{K}\alpha _{i}\right)}{\prod _{i=1}^{K}\Gamma (\alpha _{i})}}{\frac {\prod _{i=1}^{K}\Gamma (n_{j,(\cdot )}^{i}+\alpha _{i})}{\Gamma \left(\sum _{i=1}^{K}n_{j,(\cdot )}^{i}+\alpha _{i}\right)}}.\end{aligned}}}
16023:). STTM includes these following algorithms: Dirichlet Multinomial Mixture (DMM) in conference KDD2014, Biterm Topic Model (BTM) in journal TKDE2016, Word Network Topic Model (WNTM) in journal KAIS2018, Pseudo-Document-Based Topic Model (PTM) in conference KDD2016, Self-Aggregation-Based Topic Model (SATM) in conference IJCAI2015, (ETM) in conference PAKDD2017, Generalized P´olya Urn (GPU) based Dirichlet Multinomial Mixturemodel (GPU-DMM) in conference SIGIR2016, Generalized P´olya Urn (GPU) based Poisson-based Dirichlet Multinomial Mixturemodel (GPU-PDMM) in journal TIS2017 and Latent Feature Model with DMM (LF-DMM) in journal TACL2015. STTM also includes six short text corpus for evaluation. STTM presents three aspects about how to evaluate the performance of the algorithms (i.e., topic coherence, clustering, and classification).
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4999:{\displaystyle {\begin{aligned}&P({\boldsymbol {Z}},{\boldsymbol {W}};\alpha ,\beta )=\int _{\boldsymbol {\theta }}\int _{\boldsymbol {\varphi }}P({\boldsymbol {W}},{\boldsymbol {Z}},{\boldsymbol {\theta }},{\boldsymbol {\varphi }};\alpha ,\beta )\,d{\boldsymbol {\varphi }}\,d{\boldsymbol {\theta }}\\={}&\int _{\boldsymbol {\varphi }}\prod _{i=1}^{K}P(\varphi _{i};\beta )\prod _{j=1}^{M}\prod _{t=1}^{N}P(W_{j,t}\mid \varphi _{Z_{j,t}})\,d{\boldsymbol {\varphi }}\int _{\boldsymbol {\theta }}\prod _{j=1}^{M}P(\theta _{j};\alpha )\prod _{t=1}^{N}P(Z_{j,t}\mid \theta _{j})\,d{\boldsymbol {\theta }}.\end{aligned}}}
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10246:{\displaystyle P({\boldsymbol {Z}},{\boldsymbol {W}};\alpha ,\beta )=\prod _{j=1}^{M}{\frac {\Gamma \left(\sum _{i=1}^{K}\alpha _{i}\right)}{\prod _{i=1}^{K}\Gamma (\alpha _{i})}}{\frac {\prod _{i=1}^{K}\Gamma (n_{j,(\cdot )}^{i}+\alpha _{i})}{\Gamma \left(\sum _{i=1}^{K}n_{j,(\cdot )}^{i}+\alpha _{i}\right)}}\times \prod _{i=1}^{K}{\frac {\Gamma \left(\sum _{r=1}^{V}\beta _{r}\right)}{\prod _{r=1}^{V}\Gamma (\beta _{r})}}{\frac {\prod _{r=1}^{V}\Gamma (n_{(\cdot ),r}^{i}+\beta _{r})}{\Gamma \left(\sum _{r=1}^{V}n_{(\cdot ),r}^{i}+\beta _{r}\right)}}.}
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2963:{\displaystyle {\begin{aligned}{\boldsymbol {\varphi }}_{k=1\dots K}&\sim \operatorname {Dirichlet} _{V}({\boldsymbol {\beta }})\\{\boldsymbol {\theta }}_{d=1\dots M}&\sim \operatorname {Dirichlet} _{K}({\boldsymbol {\alpha }})\\z_{d=1\dots M,w=1\dots N_{d}}&\sim \operatorname {Categorical} _{K}({\boldsymbol {\theta }}_{d})\\w_{d=1\dots M,w=1\dots N_{d}}&\sim \operatorname {Categorical} _{V}({\boldsymbol {\varphi }}_{z_{dw}})\end{aligned}}}
6918:{\displaystyle \int _{\theta _{j}}{\frac {\Gamma \left(\sum _{i=1}^{K}\alpha _{i}\right)}{\prod _{i=1}^{K}\Gamma (\alpha _{i})}}\prod _{i=1}^{K}\theta _{j,i}^{\alpha _{i}-1}\prod _{i=1}^{K}\theta _{j,i}^{n_{j,(\cdot )}^{i}}\,d\theta _{j}=\int _{\theta _{j}}{\frac {\Gamma \left(\sum _{i=1}^{K}\alpha _{i}\right)}{\prod _{i=1}^{K}\Gamma (\alpha _{i})}}\prod _{i=1}^{K}\theta _{j,i}^{n_{j,(\cdot )}^{i}+\alpha _{i}-1}\,d\theta _{j}.}
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639:. As proposed in the original paper, a sparse Dirichlet prior can be used to model the topic-word distribution, following the intuition that the probability distribution over words in a topic is skewed, so that only a small set of words have high probability. The resulting model is the most widely applied variant of LDA today. The plate notation for this model is shown on the right, where
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398:(PGMs), the dependencies among the many variables can be captured concisely. The boxes are "plates" representing replicates, which are repeated entities. The outer plate represents documents, while the inner plate represents the repeated word positions in a given document; each position is associated with a choice of topic and word. The variable names are defined as follows:
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349:(pLSA), The pLSA model is equivalent to LDA under a uniform Dirichlet prior distribution. pLSA relies on only the first two assumptions above and does not care about the remainder. While both methods are similar in principle and require the user to specify the number of topics to be discovered before the start of training (as with
5960:{\displaystyle \int _{\theta _{j}}P(\theta _{j};\alpha )\prod _{t=1}^{N}P(Z_{j,t}\mid \theta _{j})\,d\theta _{j}=\int _{\theta _{j}}{\frac {\Gamma \left(\sum _{i=1}^{K}\alpha _{i}\right)}{\prod _{i=1}^{K}\Gamma (\alpha _{i})}}\prod _{i=1}^{K}\theta _{j,i}^{\alpha _{i}-1}\prod _{t=1}^{N}P(Z_{j,t}\mid \theta _{j})\,d\theta _{j}.}
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To actually infer the topics in a corpus, we imagine a generative process whereby the documents are created, so that we may infer, or reverse engineer, it. We imagine the generative process as follows. Documents are represented as random mixtures over latent topics, where each topic is characterized
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carried by individuals under study have origin in various extant or past populations. The model and various inference algorithms allow scientists to estimate the allele frequencies in those source populations and the origin of alleles carried by individuals under study. The source populations can be
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Recent research has been focused on speeding up the inference of latent
Dirichlet allocation to support the capture of a massive number of topics in a large number of documents. The update equation of the collapsed Gibbs sampler mentioned in the earlier section has a natural sparsity within it that
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in a language – e.g., "the", "an", "that", "are", "is", etc., – would not discriminate between topics and are usually filtered out by pre-processing before LDA is performed. Pre-processing also converts terms to their "root" lexical forms – e.g., "barks", "barking", and "barked" would be converted
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The LDA model is highly modular and can therefore be easily extended. The main field of interest is modeling relations between topics. This is achieved by using another distribution on the simplex instead of the
Dirichlet. The Correlated Topic Model follows this approach, inducing a correlation
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If the document collection is sufficiently large, LDA will discover such sets of terms (i.e., topics) based upon the co-occurrence of individual terms, though the task of assigning a meaningful label to an individual topic (i.e., that all the terms are DOG_related) is up to the user, and often
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As noted earlier, pLSA is similar to LDA. The LDA model is essentially the
Bayesian version of pLSA model. The Bayesian formulation tends to perform better on small datasets because Bayesian methods can avoid overfitting the data. For very large datasets, the results of the two models tend to
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In clinical psychology research, LDA has been used to identify common themes of self-images experienced by young people in social situations. Other social scientists have used LDA to examine large sets of topical data from discussions on social media (e.g., tweets about prescription drugs).
4500:{\displaystyle P({\boldsymbol {W}},{\boldsymbol {Z}},{\boldsymbol {\theta }},{\boldsymbol {\varphi }};\alpha ,\beta )=\prod _{i=1}^{K}P(\varphi _{i};\beta )\prod _{j=1}^{M}P(\theta _{j};\alpha )\prod _{t=1}^{N}P(Z_{j,t}\mid \theta _{j})P(W_{j,t}\mid \varphi _{Z_{j,t}}),}
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Most documents will contain only a relatively small number of topics. In the collection, e.g., individual topics will occur with differing frequencies. That is, they have a probability distribution, so that a given document is more likely to contain some topics than
5388:{\displaystyle \int _{\boldsymbol {\theta }}\prod _{j=1}^{M}P(\theta _{j};\alpha )\prod _{t=1}^{N}P(Z_{j,t}\mid \theta _{j})\,d{\boldsymbol {\theta }}=\prod _{j=1}^{M}\int _{\theta _{j}}P(\theta _{j};\alpha )\prod _{t=1}^{N}P(Z_{j,t}\mid \theta _{j})\,d\theta _{j}.}
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7223:{\displaystyle \int _{\theta _{j}}{\frac {\Gamma \left(\sum _{i=1}^{K}n_{j,(\cdot )}^{i}+\alpha _{i}\right)}{\prod _{i=1}^{K}\Gamma (n_{j,(\cdot )}^{i}+\alpha _{i})}}\prod _{i=1}^{K}\theta _{j,i}^{n_{j,(\cdot )}^{i}+\alpha _{i}-1}\,d\theta _{j}=1.}
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10646:{\displaystyle P(Z_{(m,n)}\mid {\boldsymbol {Z_{-(m,n)}}},{\boldsymbol {W}};\alpha ,\beta )={\frac {P(Z_{(m,n)},{\boldsymbol {Z_{-(m,n)}}},{\boldsymbol {W}};\alpha ,\beta )}{P({\boldsymbol {Z_{-(m,n)}}},{\boldsymbol {W}};\alpha ,\beta )}},}
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In evolutionary biology, it is often natural to assume that the geographic locations of the individuals observed bring some information about their ancestry. This is the rational of various models for geo-referenced genetic data.
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Variations on LDA have been used to automatically put natural images into categories, such as "bedroom" or "forest", by treating an image as a document, and small patches of the image as words; one of the variations is called
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time (same as the original
Collapsed Gibbs Sampler). However, if we fall into the other two buckets, we only need to check a subset of topics if we keep a record of the sparse topics. A topic can be sampled from the
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Computing probabilities allows a "generative" process by which a collection of new “synthetic documents” can be generated that would closely reflect the statistical characteristics of the original collection.
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can apply to both dogs and cats, but are more likely to refer to dogs, which are used as work animals or participate in obedience or skill competitions.) However, in a document, the accompanying presence of
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219:– is to discover topics in a collection of documents, and then automatically classify any individual document within the collection in terms of how "relevant" it is to each of the discovered topics. A
3330:{\displaystyle p(Z_{d,n}=k)\propto {\frac {\alpha \beta }{C_{k}^{\neg n}+V\beta }}+{\frac {C_{k}^{d}\beta }{C_{k}^{\neg n}+V\beta }}+{\frac {C_{k}^{w}(\alpha +C_{k}^{d})}{C_{k}^{\neg n}+V\beta }}}
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109:. In this, observations (e.g., words) are collected into documents, and each word's presence is attributable to one of the document's topics. Each document will contain a small number of topics.
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theme. There may be many more topics in the collection – e.g., related to diet, grooming, healthcare, behavior, etc. that we do not discuss for simplicity's sake. (Very common, so called
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Learning the various distributions (the set of topics, their associated word probabilities, the topic of each word, and the particular topic mixture of each document) is a problem of
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The original paper by
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Within a topic, certain terms will be used much more frequently than others. In other words, the terms within a topic will also have their own probability distribution.
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15389:"Results and Methodological Implications of the Digital Epidemiology of Prescription Drug References Among Twitter Users: Latent Dirichlet Allocation (LDA) Analyses"
14979:, whose structure is learnt from data. LDA can also be extended to a corpus in which a document includes two types of information (e.g., words and names), as in the
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In evolutionary biology and bio-medicine, the model is used to detect the presence of structured genetic variation in a group of individuals. The model assumes that
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in a topic; usually the same for all words; normally a number much less than 1, e.g. 0.001, to strongly prefer sparse word distributions, i.e. few words per topic
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in a document; usually the same for all topics; normally a number less than 1, e.g. 0.1, to prefer sparse topic distributions, i.e. few topics per document
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In practice, the optimal number of populations or topics is not known beforehand. It can be estimated by approximation of the posterior distribution with
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Open source Java-based package from the
University of Massachusetts-Amherst for topic modeling with LDA, also has an independently developed GUI, the
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instead of the
Dirichlet. Another extension is the hierarchical LDA (hLDA), where topics are joined together in a hierarchy by using the nested
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as matrices created by decomposing the original document-word matrix that represents the corpus of documents being modeled. In this view,
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Lamba, Manika; Madhusudhan, Margam (2019). "Mapping of topics in DESIDOC Journal of
Library and Information Technology, India: a study".
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document. Now we replace the probabilities in the above equation by the true distribution expression to write out the explicit equation.
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Dirichlet
Multinomial Mixture model. jLDADMM also provides an implementation for document clustering evaluation to compare topic models.
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to represent a document in the training set. So in pLSA, when presented with a document the model has not seen before, we fix
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A Java package for topic modeling on normal or short texts. jLDADMM includes implementations of the LDA topic model and the
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is small, we are very unlikely to fall into this bucket; however, if we do fall into this bucket, sampling a topic takes
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is three dimensional. If any of the three dimensions is not limited to a specific value, we use a parenthesized point
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is considered to be a set of terms (i.e., individual words or phrases) that, taken together, suggest a shared theme.
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15699:. Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence. San Francisco, CA: Morgan Kaufmann.
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s will be integrated out. For simplicity, in this derivation the documents are all assumed to have the same length
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A direct optimization of the likelihood with a block relaxation algorithm proves to be a fast alternative to MCMC.
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5549:{\displaystyle \int _{\theta _{j}}P(\theta _{j};\alpha )\prod _{t=1}^{N}P(Z_{j,t}\mid \theta _{j})\,d\theta _{j}.}
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15228:"Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies"
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In this equation, we have three terms, out of which two are sparse, and the other is small. We call these terms
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When LDA machine learning is employed, both sets of probabilities are computed during the training phase, using
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6410:{\displaystyle \prod _{t=1}^{N}P(Z_{j,t}\mid \theta _{j})=\prod _{i=1}^{K}\theta _{j,i}^{n_{j,(\cdot )}^{i}}.}
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Proceedings of the 2005 IEEE Computer
Society Conference on Computer Vision and Pattern Recognition (CVPR'05)
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requires specialized knowledge (e.g., for collection of technical documents). The LDA approach assumes that:
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15101:. Blei argues that this step is cheating because you are essentially refitting the model to the new data.
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15809:. Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference. MIT Press.
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The semantic content of a document is composed by combining one or more terms from one or more topics.
105:) for modeling automatically extracted topics in textual corpora. The LDA is an example of a Bayesian
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11039:{\displaystyle P\left(Z_{m,n}\mid {\boldsymbol {Z_{-(m,n)}}},{\boldsymbol {W}};\alpha ,\beta \right)}
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Li, Fei-Fei; Perona, Pietro. "A Bayesian Hierarchical Model for Learning Natural Scene Categories".
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727:-dimensional vectors storing the parameters of the Dirichlet-distributed topic-word distributions (
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denotes the number of topics assigned to the current document and current word type respectively.
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using linked data and semantic web technology. Related models and techniques are, among others,
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by a distribution over all the words. LDA assumes the following generative process for a corpus
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3691:{\displaystyle C=\sum _{k=1}^{K}{\frac {C_{k}^{w}(\alpha +C_{k}^{d})}{C_{k}^{\neg n}+V\beta }}}
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LDA yields better disambiguation of words and a more precise assignment of documents to topics.
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can be taken advantage of. Intuitively, since each document only contains a subset of topics
2985:
1134:
793:
753:
122:
10778:
10748:
10718:
6239:
6209:
6071:
6041:
6011:
5562:
3824:
1272:
604:
562:
523:
16834:
16510:
16486:
16339:
15539:
4094:
4067:
3082:
3055:
1833:
1576:
974:
418:
363:
Unlike LDA, pLSA is vulnerable to overfitting especially when the size of corpus increases.
4205:
4124:
3925:
8:
16814:
16744:
16701:
16657:
16429:
16419:
16414:
16302:
16056:
15387:
Parker, Maria A.; Valdez, Danny; Rao, Varun K.; Eddens, Katherine S.; Agley, Jon (2023).
15132:
3343:
1295:
632:
171:
118:
16090:
Latent Dirichlet Allocation (LDA) Tutorial for the Infer.NET Machine Computing Framework
15543:
15415:
15388:
16824:
16696:
16561:
16324:
16307:
16165:
15752:
15667:
15638:
15611:
15586:
15499:
15479:
15428:
15364:
15337:
15256:
15227:
15203:
15084:
14994:
14987:
mixture model, which allows the number of topics to be unbounded and learnt from data.
12811:
10852:
10698:
4011:
3955:
3905:
3784:
3764:
3744:
3724:
3704:
3369:
1808:
1728:
1699:
1670:
1623:
1603:
954:
934:
730:
710:
642:
366:
The LDA algorithm is more readily amenable to scaling up for large data sets using the
350:
335:
314:, belonging to more than one topic, with different probability. (For example, the term
15562:
15527:
16829:
16541:
16349:
16260:
15877:
15872:
15851:
15810:
15797:
15756:
15700:
15672:
15616:
15567:
15506:
15483:
15432:
15420:
15369:
15285:
15261:
15208:
15190:
3386:
respectively. Now, if we normalize each term by summing over all the topics, we get:
3009:
876:
refers to a set of rows, or vectors, each of which is a distribution over words, and
149:
16706:
16591:
16566:
16367:
16270:
16039:
An exhaustive list of LDA-related resources (incl. papers and some implementations)
15867:
15744:
15662:
15654:
15606:
15598:
15557:
15547:
15469:
15410:
15400:
15359:
15351:
15315:
15307:
15251:
15243:
15198:
15182:
14927:
3106:, the above update equation could be rewritten to take advantage of this sparsity.
208:
137:
102:
98:
15724:. 15th ACM SIGKDD international conference on Knowledge discovery and data mining.
15247:
12804:
excluded. The above equation can be further simplified leveraging the property of
3567:{\displaystyle B=\sum _{k=1}^{K}{\frac {C_{k}^{d}\beta }{C_{k}^{\neg n}+V\beta }}}
16818:
16779:
16774:
16642:
16372:
16245:
16220:
16202:
16070:
15186:
4510:
where the bold-font variables denote the vector version of the variables. First,
636:
323:
neighboring terms (which belong to only one topic) will disambiguate their usage.
15658:
15311:
3467:{\displaystyle A=\sum _{k=1}^{K}{\frac {\alpha \beta }{C_{k}^{\neg n}+V\beta }}}
16526:
16506:
16230:
16036:
16031:
15748:
15355:
12805:
2997:
448:
is the parameter of the Dirichlet prior on the per-document topic distributions
391:
383:
130:
16115:
379:
16880:
16789:
16601:
16581:
16362:
16027:
15722:
Efficient methods for topic model inference on streaming document collections
15194:
1548:{\displaystyle w_{i,j}\sim \operatorname {Multinomial} (\varphi _{z_{i,j}}).}
15552:
15505:. Proceedings of SIGIR 2003. New York: Association for Computing Machinery.
10401:
directly. The key point is to derive the following conditional probability:
6266:
topic. Thus, the right most part of the above equation can be rewritten as:
16769:
16387:
16099:
16006:
15881:
15676:
15620:
15571:
15424:
15373:
15265:
15212:
4121:
Notice that after sampling each topic, updating these buckets is all basic
3844:
Now, while sampling a topic, if we sample a random variable uniformly from
810:
consists of rows defined by documents and columns defined by topics, while
15602:
597:
Plate notation for LDA with Dirichlet-distributed topic-word distributions
454:
is the parameter of the Dirichlet prior on the per-topic word distribution
16726:
16606:
16319:
16212:
16160:
15457:
Transactions of the International Society for Music Information Retrieval
10350:
is invariable for any of Z, Gibbs Sampling equations can be derived from
10300:{\displaystyle P({\boldsymbol {Z}}\mid {\boldsymbol {W}};\alpha ,\beta )}
212:
181:
175:
106:
15166:
10947:, according to the above probability, we do not need the exact value of
1640:). The subscript is often dropped, as in the plate diagrams shown here.
226:
For example, in a document collection related to pet animals, the terms
16329:
16066:
1600:
are treated as independent of all the other data generating variables (
830:
consists of rows defined by topics and columns defined by words. Thus,
295:
199:, LDA has been used to discover tonal structures in different corpora.
141:
16049:
implementation of online LDA for inputs larger than the available RAM.
15923:
Proceedings of Neural Information Processing Systems Conference (NIPS)
15474:
15451:
10256:
The goal of Gibbs Sampling here is to approximate the distribution of
1470:{\displaystyle z_{i,j}\sim \operatorname {Multinomial} (\theta _{i}).}
922:
refers to a set of rows, each of which is a distribution over topics.
16197:
16079:
15839:. 25th IEEE International Conference on Data Engineering (ICDE 2009).
15338:"Characterising Negative Mental Imagery in Adolescent Social Anxiety"
15142:
10394:{\displaystyle P({\boldsymbol {Z}},{\boldsymbol {W}};\alpha ,\beta )}
2640:
We can then mathematically describe the random variables as follows:
367:
145:
16020:
170:
interpreted ex-post in terms of various evolutionary scenarios. In
16672:
16652:
16637:
16616:
16586:
16531:
16496:
16377:
15973:
external links, and converting useful links where appropriate into
15803:
Hierarchical Topic Models and the Nested Chinese Restaurant Process
15587:"Fast model-based estimation of ancestry in unrelated individuals"
15405:
15167:"Inference of population structure using multilocus genotype data"
12808:. We first split the summation and then merge it back to obtain a
10775:
document. And further we assume that the word symbol of it is the
16809:
16667:
16647:
16521:
16265:
16180:
4226:. The derivation is equally valid if the document lengths vary.
16175:
16170:
16083:
16042:
15719:
15137:
4229:
According to the model, the total probability of the model is:
3801:
on the other hand, is dense but because of the small values of
593:
166:
16865:
16501:
16046:
11088:
can take value. So, the above equation can be simplified as:
9616:
For clarity, here we write down the final equation with both
6928:
The equation inside the integration has the same form as the
1721:
number of words in the vocabulary (e.g. 50,000 or 1,000,000)
1218:{\displaystyle \varphi _{k}\sim \operatorname {Dir} (\beta )}
1039:{\displaystyle \theta _{i}\sim \operatorname {Dir} (\alpha )}
15585:
Alexander, David H.; Novembre, John; Lange, Kenneth (2009).
14918:
Note that the same formula is derived in the article on the
10908:. Note that Gibbs Sampling needs only to sample a value for
3841:, the value is very small compared to the two other terms.
16026:
Lecture that covers some of the notation in this article:
15849:
15636:
15165:
Pritchard, J. K.; Stephens, M.; Donnelly, P. (June 2000).
16662:
15850:
Guillot, G.; Leblois, R.; Coulon, A.; Frantz, A. (2009).
15637:
Guillot, G.; Estoup, A.; Mortier, F.; Cosson, J. (2005).
15164:
2388:-dimensional vector of probabilities, which must sum to 1
2259:-dimensional vector of probabilities, which must sum to 1
5029:
s are independent to each other and the same to all the
160:
15693:
Expectation-propagation for the generative aspect model
15225:
14943:
Topic modeling is a classic solution to the problem of
3902:, we can check which bucket our sample lands in. Since
3027:
15829:
15584:
15526:
Griffiths, Thomas L.; Steyvers, Mark (April 6, 2004).
14991:
converge. One difference is that pLSA uses a variable
14922:, as part of a more general discussion of integrating
750:
It is helpful to think of the entities represented by
182:
Clinical psychology, mental health, and social science
15769:
15386:
15336:
Chiu, Kin; Clark, David; Leigh, Eleanor (July 2022).
15087:
15052:
15017:
14997:
12837:
12814:
12771:
12733:
12674:
11097:
11055:
10956:
10914:
10875:
10855:
10811:
10781:
10751:
10721:
10701:
10662:
10410:
10356:
10313:
10262:
9669:
9644:
9622:
8449:
8440:
part. Here we only list the steps of the derivation:
8424:
8402:
8380:
7242:
6945:
6456:
6426:
6275:
6242:
6212:
6168:
6142:
6104:
6074:
6044:
6014:
5976:
5598:
5565:
5559:
Actually, it is the hidden part of the model for the
5427:
5404:
5118:
5095:
5075:
5055:
5035:
5015:
4563:
4538:
4516:
4238:
4208:
4188:
4168:
4127:
4097:
4070:
4034:
4014:
3978:
3958:
3928:
3908:
3850:
3827:
3807:
3787:
3767:
3761:
is also a sparse summation of the topics that a word
3747:
3727:
3721:
is a summation of the topics that appear in document
3707:
3583:
3483:
3395:
3372:
3346:
3115:
3085:
3079:, and a word also only appears in a subset of topics
3058:
2649:
2607:
2524:
2487:
2404:
2348:
2275:
2247:{\displaystyle {\boldsymbol {\varphi }}_{k=1\dots K}}
2219:
2146:
2115:
2085:
2033:
2002:
1972:
1920:
1863:
1836:
1811:
1760:
1731:
1702:
1673:
1626:
1606:
1579:
1487:
1422:
1368:
1324:
1298:
1275:
1231:
1186:
1157:
1137:
1096:
1052:
1007:
977:
957:
937:
882:
836:
816:
796:
776:
756:
733:
713:
665:
645:
607:
565:
526:
494:
462:
421:
15800:; Griffiths, Thomas L.; Tenenbaum, Joshua B (2004).
15639:"A spatial statistical model for landscape genetics"
2376:{\displaystyle {\boldsymbol {\theta }}_{d=1\dots M}}
16028:
LDA and Topic Modelling Video Lecture by David Blei
15833:
A Latent Topic Model for Complete Entity Resolution
15720:Yao, Limin; Mimno, David; McCallum, Andrew (2009).
15689:
15226:Falush, D.; Stephens, M.; Pritchard, J. K. (2003).
10343:{\displaystyle P({\boldsymbol {W}};\alpha ,\beta )}
4153:
1830:total number of words in all documents; sum of all
41:
may be too technical for most readers to understand
15498:
15445:
15093:
15073:
15038:
15003:
14907:
12820:
12796:
12757:
12719:
12657:
11080:
11038:
10939:
10900:
10861:
10841:
10797:
10767:
10737:
10707:
10687:
10645:
10393:
10342:
10299:
10245:
9652:
9630:
9605:
8432:
8410:
8388:
8363:
7222:
6917:
6439:
6409:
6258:
6228:
6198:
6154:
6128:
6090:
6060:
6030:
6000:
5959:
5581:
5548:
5410:
5387:
5101:
5081:
5061:
5041:
5021:
4998:
4546:
4524:
4499:
4218:
4194:
4174:
4142:
4110:
4083:
4056:
4020:
4000:
3964:
3943:
3914:
3894:
3833:
3813:
3793:
3773:
3753:
3733:
3713:
3690:
3566:
3466:
3378:
3358:
3329:
3098:
3071:
2962:
2615:
2574:
2495:
2454:
2375:
2318:
2246:
2189:
2128:
2093:
2058:
2015:
1980:
1945:
1903:
1849:
1817:
1785:
1737:
1708:
1679:
1632:
1612:
1592:
1547:
1469:
1405:
1354:
1310:
1281:
1261:
1217:
1169:
1143:
1119:
1082:
1038:
990:
963:
943:
914:
868:
822:
802:
782:
762:
739:
719:
697:
651:
623:
581:
542:
507:
475:
434:
15957:may not follow Knowledge's policies or guidelines
15913:
15779:Advances in Neural Information Processing Systems
15525:
15496:
11259:
11247:
11162:
11150:
11005:
10993:
10833:
10821:
10608:
10596:
10550:
10538:
10463:
10451:
4158:Following is the derivation of the equations for
411:is number of words in a given document (document
16878:
16348:
16092:Microsoft Research C# Machine Learning Framework
15734:
15329:
15053:
15018:
12828:-independent summation, which could be dropped:
2514:identity of topic of all words in all documents
2190:{\displaystyle \varphi _{k=1\dots K,w=1\dots V}}
1566:with only one trial, which is also known as the
869:{\displaystyle \varphi _{1},\dots ,\varphi _{K}}
698:{\displaystyle \varphi _{1},\dots ,\varphi _{K}}
16145:
15532:Proceedings of the National Academy of Sciences
15160:
15158:
2319:{\displaystyle \theta _{d=1\dots M,k=1\dots K}}
601:The fact that W is grayed out means that words
16019:A Java package for short text topic modeling (
15632:
15630:
15335:
14983:. Nonparametric extensions of LDA include the
2628:-dimensional vector of integers between 1 and
2508:-dimensional vector of integers between 1 and
915:{\displaystyle \theta _{1},\dots ,\theta _{M}}
353:) LDA has the following advantages over pLSA:
16131:
15830:Shu, Liangcai; Long, Bo; Meng, Weiyi (2009).
15439:
2575:{\displaystyle w_{d=1\dots M,w=1\dots N_{d}}}
2455:{\displaystyle z_{d=1\dots M,w=1\dots N_{d}}}
345:LDA is a generalization of older approach of
15894:
15283:
15219:
15155:
11049:but the ratios among the probabilities that
1400:
1375:
1349:
1331:
1256:
1238:
1077:
1059:
15843:
15627:
1648:A formal description of LDA is as follows:
747:is the number of words in the vocabulary).
16138:
16124:
15770:Blei, David M.; Lafferty, John D. (2005).
15279:
15277:
15275:
10842:{\displaystyle {\boldsymbol {Z_{-(m,n)}}}}
4008:time, and a topic can be sampled from the
3019:
15993:Learn how and when to remove this message
15871:
15852:"Statistical methods in spatial genetics"
15666:
15610:
15561:
15551:
15473:
15414:
15404:
15363:
15255:
15202:
9304:
9062:
8784:
8606:
8085:
7562:
7351:
7200:
6898:
6687:
6206:denotes the number of word tokens in the
5940:
5700:
5529:
5368:
5234:
4980:
4856:
4685:
4676:
3039:
2991:
2328:probability (real number between 0 and 1)
2199:probability (real number between 0 and 1)
69:Learn how and when to remove this message
53:, without removing the technical details.
8411:{\displaystyle {\boldsymbol {\varphi }}}
8389:{\displaystyle {\boldsymbol {\varphi }}}
6068:word in the vocabulary) assigned to the
6038:document with the same word symbol (the
4525:{\displaystyle {\boldsymbol {\varphi }}}
3781:is assigned to across the whole corpus.
3034:reversible-jump Markov chain Monte Carlo
592:
378:
16887:Statistical natural language processing
15272:
11269:
11256:
11250:
11240:
11172:
11159:
11153:
11143:
11015:
11002:
10996:
10986:
10830:
10824:
10814:
10618:
10605:
10599:
10589:
10560:
10547:
10541:
10531:
10473:
10460:
10454:
10444:
10372:
10364:
10321:
10278:
10270:
9685:
9677:
9653:{\displaystyle {\boldsymbol {\theta }}}
9646:
9624:
8611:
8462:
8433:{\displaystyle {\boldsymbol {\theta }}}
8426:
8404:
8382:
6447:integration formula can be changed to:
5239:
5124:
4985:
4870:
4861:
4712:
4690:
4681:
4657:
4649:
4641:
4633:
4621:
4611:
4584:
4576:
4547:{\displaystyle {\boldsymbol {\theta }}}
4540:
4518:
4270:
4262:
4254:
4246:
2933:
2844:
2761:
2715:
2702:
2656:
2634:identity of all words in all documents
2351:
2222:
2087:
1981:{\displaystyle {\boldsymbol {\alpha }}}
1974:
1406:{\displaystyle j\in \{1,\dots ,N_{i}\}}
1120:{\displaystyle \mathrm {Dir} (\alpha )}
483:is the topic distribution for document
16879:
16102:also features an implementation of LDA
15914:Wang, Xiaogang; Grimson, Eric (2007).
15690:Minka, Thomas; Lafferty, John (2002).
15501:On an Equivalence between PLSI and LDA
15288:(January 2003). Lafferty, John (ed.).
14971:structure between topics by using the
14957:probabilistic latent semantic indexing
8396:part. Actually, the derivation of the
2094:{\displaystyle {\boldsymbol {\beta }}}
1904:{\displaystyle N=\sum _{d=1}^{M}N_{d}}
347:probabilistic latent semantic analysis
16119:
15916:"Spatial Latent Dirichlet Allocation"
9631:{\displaystyle {\boldsymbol {\phi }}}
5089:separately. We now focus only on the
3895:{\displaystyle s\sim U(s|\mid A+B+C)}
2106:-dimensional vector of positive reals
1993:-dimensional vector of positive reals
1652:Definition of variables in the model
925:
161:Evolutionary biology and bio-medicine
51:make it understandable to non-experts
16597:Simple Knowledge Organization System
15937:
15393:Journal of Medical Internet Research
15295:Journal of Machine Learning Research
6008:be the number of word tokens in the
3028:Unknown number of populations/topics
3003:
1946:{\displaystyle \alpha _{k=1\dots K}}
25:
15116:spatial latent Dirichlet allocation
14933:
2391:distribution of topics in document
2059:{\displaystyle \beta _{w=1\dots V}}
1355:{\displaystyle i\in \{1,\dots ,M\}}
1262:{\displaystyle k\in \{1,\dots ,K\}}
1083:{\displaystyle i\in \{1,\dots ,M\}}
515:is the word distribution for topic
394:, which is often used to represent
202:
13:
15497:Girolami, Mark; Kaban, A. (2003).
14920:Dirichlet-multinomial distribution
14349:
14273:
14186:
13846:
13770:
13693:
13595:
13519:
13426:
13296:
13214:
13131:
13033:
12957:
12864:
12720:{\displaystyle n_{j,r}^{i,-(m,n)}}
12571:
12516:
12439:
12331:
12276:
12175:
12120:
12009:
11954:
11853:
11798:
11721:
11652:
11585:
11500:
11445:
11373:
11306:
10163:
10112:
10066:
9999:
9895:
9844:
9798:
9731:
9519:
9468:
9422:
9355:
9197:
9130:
8919:
8852:
8277:
8226:
8180:
8113:
7948:
7851:
7754:
7703:
7657:
7590:
7455:
7388:
7063:
6966:
6791:
6724:
6544:
6477:
6199:{\displaystyle n_{j,(\cdot )}^{i}}
5804:
5737:
3668:
3544:
3444:
3307:
3227:
3171:
2980:Dirichlet-multinomial distribution
2136:values, viewed as a single vector
2023:values, viewed as a single vector
1292:3. For each of the word positions
1104:
1101:
1098:
14:
16908:
16612:Thesaurus (information retrieval)
16021:https://github.com/qiang2100/STTM
15933:
15104:
14961:non-negative matrix factorization
14938:
8374:Now we turn our attention to the
5398:We can further focus on only one
659:denotes the number of topics and
15942:
15873:10.1111/j.1365-294X.2009.04410.x
15446:Lieck, Robert; Moss, Fabian C.;
11253:
11244:
11156:
11147:
10999:
10990:
10827:
10818:
10602:
10593:
10544:
10535:
10457:
10448:
4154:Aspects of computational details
3044:Alternative approaches include
2609:
2489:
370:approach on a computing cluster.
30:
15907:
15888:
15823:
15789:
15763:
15728:
15713:
15683:
15284:Blei, David M.; Ng, Andrew Y.;
2262:distribution of words in topic
405:denotes the number of documents
16193:Natural language understanding
15578:
15519:
15490:
15380:
15343:Cognitive Therapy and Research
15081:—the topic distribution under
15068:
15056:
15033:
15021:
14985:hierarchical Dirichlet process
14953:independent component analysis
14880:
14868:
14848:
14842:
14796:
14784:
14764:
14758:
14727:
14715:
14701:
14695:
14648:
14636:
14616:
14610:
14564:
14552:
14532:
14526:
14495:
14483:
14469:
14463:
14421:
14409:
14389:
14383:
14324:
14312:
14292:
14286:
14237:
14225:
14211:
14205:
14145:
14133:
14113:
14107:
14061:
14049:
14029:
14023:
13992:
13980:
13966:
13960:
13918:
13906:
13886:
13880:
13821:
13809:
13789:
13783:
13744:
13732:
13718:
13712:
13667:
13655:
13635:
13629:
13570:
13558:
13538:
13532:
13477:
13465:
13451:
13445:
13368:
13356:
13336:
13330:
13265:
13253:
13233:
13227:
13182:
13170:
13156:
13150:
13105:
13093:
13073:
13067:
13008:
12996:
12976:
12970:
12915:
12903:
12889:
12883:
12789:
12777:
12712:
12700:
12611:
12605:
12535:
12529:
12464:
12458:
12371:
12365:
12295:
12289:
12221:
12215:
12145:
12139:
12049:
12043:
11973:
11967:
11899:
11893:
11823:
11817:
11740:
11734:
11668:
11655:
11546:
11540:
11470:
11464:
11389:
11376:
11285:
11224:
11212:
11204:
11188:
11127:
11115:
11105:
11073:
11061:
10932:
10920:
10893:
10881:
10680:
10668:
10634:
10584:
10576:
10521:
10509:
10501:
10489:
10434:
10422:
10414:
10388:
10360:
10337:
10317:
10294:
10266:
10203:
10197:
10158:
10129:
10123:
10115:
10082:
10069:
9941:
9935:
9890:
9867:
9861:
9847:
9814:
9801:
9701:
9673:
9559:
9553:
9514:
9485:
9479:
9471:
9438:
9425:
9267:
9261:
9213:
9200:
9044:
9038:
8935:
8922:
8781:
8736:
8688:
8669:
8603:
8558:
8510:
8491:
8323:
8317:
8272:
8249:
8243:
8229:
8196:
8183:
8054:
8048:
7994:
7971:
7965:
7951:
7897:
7891:
7800:
7794:
7749:
7726:
7720:
7706:
7673:
7660:
7531:
7525:
7471:
7458:
7348:
7316:
7289:
7270:
7169:
7163:
7109:
7086:
7080:
7066:
7012:
7006:
6867:
6861:
6807:
6794:
6675:
6669:
6560:
6547:
6392:
6386:
6332:
6300:
6186:
6180:
6149:
6143:
5937:
5905:
5820:
5807:
5697:
5665:
5638:
5619:
5526:
5494:
5467:
5448:
5365:
5333:
5306:
5287:
5231:
5199:
5172:
5153:
4977:
4945:
4918:
4899:
4853:
4808:
4760:
4741:
4673:
4629:
4600:
4572:
4491:
4446:
4440:
4408:
4381:
4362:
4335:
4316:
4286:
4242:
4137:
4131:
4051:
4038:
3995:
3982:
3938:
3932:
3889:
3867:
3860:
3653:
3629:
3292:
3268:
3144:
3119:
2953:
2928:
2854:
2839:
2765:
2757:
2706:
2698:
1786:{\displaystyle N_{d=1\dots M}}
1539:
1513:
1461:
1448:
1212:
1206:
1114:
1108:
1033:
1027:
635:, and the other variables are
396:probabilistic graphical models
1:
16717:Optical character recognition
15290:"Latent Dirichlet Allocation"
15148:
3008:The original ML paper used a
1643:
190:
16410:Multi-document summarization
16078:implementation of LDA using
15128:Variational Bayesian methods
15074:{\displaystyle \Pr(z\mid d)}
15039:{\displaystyle \Pr(w\mid z)}
14973:logistic normal distribution
8418:part is very similar to the
2973:
2616:{\displaystyle \mathbf {W} }
2496:{\displaystyle \mathbf {Z} }
1798:number of words in document
1170:{\displaystyle \alpha <1}
508:{\displaystyle \varphi _{k}}
103:generative statistical model
21:linear discriminant analysis
7:
16740:Latent Dirichlet allocation
16712:Natural language generation
16577:Machine-readable dictionary
16572:Linguistic Linked Open Data
16147:Natural language processing
16037:D. Mimno's LDA Bibliography
15659:10.1534/genetics.104.033803
15528:"Finding scientific topics"
15452:"The Tonal Diffusion Model"
15312:10.1162/jmlr.2003.3.4-5.993
15248:10.1093/genetics/164.4.1567
15121:
12758:{\displaystyle n_{j,r}^{i}}
6440:{\displaystyle \theta _{j}}
6129:{\displaystyle n_{j,r}^{i}}
6001:{\displaystyle n_{j,r}^{i}}
4554:need to be integrated out.
2016:{\displaystyle \alpha _{k}}
1692:number of topics (e.g. 50)
1151:which typically is sparse (
1131:with a symmetric parameter
476:{\displaystyle \theta _{i}}
217:natural language processing
155:
83:natural language processing
10:
16913:
16492:Explicit semantic analysis
16241:Deep linguistic processing
16063:packages for LDA analysis.
15749:10.1007/s11192-019-03137-5
15356:10.1007/s10608-022-10316-x
15187:10.1093/genetics/155.2.945
14977:Chinese restaurant process
14965:Gamma-Poisson distribution
2977:
2470:identity of topic of word
2129:{\displaystyle \beta _{w}}
386:representing the LDA model
207:One application of LDA in
112:
18:
16843:
16798:
16753:
16725:
16685:
16630:
16552:
16540:
16471:
16428:
16400:
16335:Word-sense disambiguation
16211:
16188:Computational linguistics
16153:
15772:"Correlated topic models"
12797:{\displaystyle Z_{(m,n)}}
11081:{\displaystyle Z_{(m,n)}}
10940:{\displaystyle Z_{(m,n)}}
10901:{\displaystyle Z_{(m,n)}}
10688:{\displaystyle Z_{(m,n)}}
6236:document assigned to the
971:documents each of length
16861:Natural Language Toolkit
16785:Pronunciation assessment
16687:Automatic identification
16517:Latent semantic analysis
16473:Distributional semantics
16358:Compound-term processing
16256:Named-entity recognition
14949:latent semantic indexing
10805:word in the vocabulary.
6162:to denote. For example,
6155:{\displaystyle (\cdot )}
5082:{\displaystyle \varphi }
5049:s. So we can treat each
5042:{\displaystyle \varphi }
4175:{\displaystyle \varphi }
4160:collapsed Gibbs sampling
4057:{\displaystyle O(K_{w})}
4001:{\displaystyle O(K_{d})}
1568:categorical distribution
1560:multinomial distribution
823:{\displaystyle \varphi }
783:{\displaystyle \varphi }
374:
340:Expectation Maximization
197:computational musicology
19:Not to be confused with
16765:Automated essay scoring
16735:Document classification
16402:Automatic summarization
16098:: Since version 1.3.0,
16032:same lecture on YouTube
15553:10.1073/pnas.0307752101
15538:(Suppl. 1): 5228–5235.
12727:be the same meaning as
10715:hidden variable of the
5411:{\displaystyle \theta }
5102:{\displaystyle \theta }
5062:{\displaystyle \theta }
5022:{\displaystyle \theta }
4195:{\displaystyle \theta }
4150:arithmetic operations.
3814:{\displaystyle \alpha }
3046:expectation propagation
3020:Likelihood maximization
1144:{\displaystyle \alpha }
803:{\displaystyle \theta }
763:{\displaystyle \theta }
258:theme, while the terms
16892:Latent variable models
16622:Universal Dependencies
16315:Terminology extraction
16298:Semantic decomposition
16293:Semantic role labeling
16283:Part-of-speech tagging
16251:Information extraction
16236:Coreference resolution
16226:Collocation extraction
16011:one-topic-per-document
15095:
15075:
15040:
15005:
14924:Dirichlet distribution
14909:
14836:
14604:
14377:
14101:
13874:
13623:
13324:
13061:
12822:
12798:
12759:
12721:
12659:
12599:
12512:
12438:
12359:
12272:
12203:
12119:
12037:
11950:
11881:
11797:
11704:
11651:
11613:
11528:
11444:
11372:
11334:
11082:
11040:
10941:
10902:
10863:
10843:
10799:
10798:{\displaystyle v^{th}}
10769:
10768:{\displaystyle m^{th}}
10739:
10738:{\displaystyle n^{th}}
10709:
10689:
10647:
10395:
10344:
10301:
10247:
10191:
10111:
10065:
10027:
9995:
9923:
9843:
9797:
9759:
9727:
9654:
9632:
9607:
9547:
9467:
9421:
9383:
9351:
9239:
9196:
9158:
9109:
9016:
8961:
8918:
8880:
8831:
8732:
8711:
8648:
8554:
8533:
8487:
8434:
8412:
8390:
8365:
8305:
8225:
8179:
8141:
8020:
7947:
7879:
7782:
7702:
7656:
7618:
7497:
7454:
7416:
7312:
7224:
7135:
7062:
6994:
6934:Dirichlet distribution
6930:Dirichlet distribution
6919:
6833:
6790:
6752:
6641:
6586:
6543:
6505:
6441:
6411:
6358:
6296:
6260:
6259:{\displaystyle i^{th}}
6230:
6229:{\displaystyle j^{th}}
6200:
6156:
6130:
6092:
6091:{\displaystyle i^{th}}
6062:
6061:{\displaystyle r^{th}}
6032:
6031:{\displaystyle j^{th}}
6002:
5961:
5901:
5846:
5803:
5765:
5661:
5583:
5582:{\displaystyle j^{th}}
5550:
5490:
5412:
5389:
5329:
5266:
5195:
5149:
5103:
5083:
5063:
5043:
5023:
5000:
4941:
4895:
4804:
4783:
4737:
4548:
4526:
4501:
4404:
4358:
4312:
4220:
4196:
4176:
4144:
4112:
4085:
4058:
4022:
4002:
3966:
3945:
3916:
3896:
3835:
3834:{\displaystyle \beta }
3815:
3795:
3775:
3755:
3735:
3715:
3701:Here, we can see that
3692:
3610:
3568:
3510:
3468:
3422:
3380:
3360:
3331:
3100:
3073:
3040:Alternative approaches
3014:posterior distribution
2992:Monte Carlo simulation
2964:
2617:
2584:integer between 1 and
2576:
2497:
2464:integer between 1 and
2456:
2377:
2335:occurring in document
2320:
2248:
2191:
2130:
2095:
2060:
2017:
1982:
1958:prior weight of topic
1947:
1905:
1890:
1851:
1819:
1787:
1739:
1710:
1681:
1634:
1614:
1594:
1549:
1471:
1407:
1356:
1312:
1283:
1282:{\displaystyle \beta }
1263:
1219:
1171:
1145:
1129:Dirichlet distribution
1121:
1084:
1040:
992:
965:
945:
916:
870:
824:
804:
784:
764:
741:
721:
699:
653:
625:
624:{\displaystyle w_{ij}}
598:
583:
582:{\displaystyle w_{ij}}
544:
543:{\displaystyle z_{ij}}
509:
477:
436:
387:
121:, LDA was proposed by
16383:Sentence segmentation
16112:MATLAB implementation
15603:10.1101/gr.094052.109
15096:
15076:
15041:
15006:
14945:information retrieval
14910:
14816:
14584:
14357:
14081:
13854:
13603:
13304:
13041:
12823:
12799:
12760:
12722:
12660:
12579:
12492:
12418:
12339:
12252:
12183:
12099:
12017:
11930:
11861:
11777:
11684:
11631:
11593:
11508:
11424:
11352:
11314:
11083:
11041:
10942:
10903:
10864:
10844:
10800:
10770:
10740:
10710:
10690:
10648:
10396:
10345:
10302:
10248:
10171:
10091:
10045:
10007:
9975:
9903:
9823:
9777:
9739:
9707:
9655:
9633:
9608:
9527:
9447:
9401:
9363:
9331:
9219:
9176:
9138:
9089:
8996:
8941:
8898:
8860:
8811:
8712:
8691:
8628:
8534:
8513:
8467:
8435:
8413:
8391:
8366:
8285:
8205:
8159:
8121:
8000:
7927:
7859:
7762:
7682:
7636:
7598:
7477:
7434:
7396:
7292:
7225:
7115:
7042:
6974:
6920:
6813:
6770:
6732:
6621:
6566:
6523:
6485:
6442:
6412:
6338:
6276:
6261:
6231:
6201:
6157:
6131:
6093:
6063:
6033:
6003:
5962:
5881:
5826:
5783:
5745:
5641:
5584:
5551:
5470:
5413:
5390:
5309:
5246:
5175:
5129:
5104:
5084:
5064:
5044:
5024:
5001:
4921:
4875:
4784:
4763:
4717:
4549:
4527:
4502:
4384:
4338:
4292:
4221:
4197:
4177:
4145:
4113:
4111:{\displaystyle K_{w}}
4086:
4084:{\displaystyle K_{d}}
4059:
4023:
4003:
3967:
3946:
3917:
3897:
3836:
3816:
3796:
3776:
3756:
3736:
3716:
3693:
3590:
3569:
3490:
3469:
3402:
3381:
3361:
3332:
3101:
3099:{\displaystyle K_{w}}
3074:
3072:{\displaystyle K_{d}}
3012:approximation of the
2986:statistical inference
2965:
2618:
2577:
2498:
2457:
2378:
2331:probability of topic
2321:
2249:
2192:
2131:
2096:
2071:prior weight of word
2061:
2018:
1983:
1948:
1906:
1870:
1852:
1850:{\displaystyle N_{d}}
1820:
1788:
1740:
1711:
1682:
1635:
1615:
1595:
1593:{\displaystyle N_{i}}
1550:
1472:
1408:
1357:
1313:
1284:
1264:
1220:
1172:
1146:
1122:
1085:
1041:
993:
991:{\displaystyle N_{i}}
966:
946:
917:
871:
825:
805:
785:
765:
742:
722:
700:
654:
626:
596:
589:is the specific word.
584:
554:-th word in document
550:is the topic for the
545:
510:
478:
437:
435:{\displaystyle N_{i}}
382:
16897:Probabilistic models
16835:Voice user interface
16546:datasets and corpora
16487:Document-term matrix
16340:Word-sense induction
15963:improve this article
15085:
15050:
15015:
14995:
12835:
12812:
12769:
12731:
12672:
11095:
11053:
10954:
10912:
10873:
10853:
10809:
10779:
10749:
10719:
10699:
10660:
10408:
10354:
10311:
10260:
9667:
9642:
9620:
8447:
8422:
8400:
8378:
7240:
6943:
6454:
6424:
6273:
6240:
6210:
6166:
6140:
6102:
6072:
6042:
6012:
5974:
5596:
5563:
5425:
5402:
5116:
5093:
5073:
5053:
5033:
5013:
4561:
4536:
4514:
4236:
4219:{\displaystyle N_{}}
4206:
4186:
4166:
4143:{\displaystyle O(1)}
4125:
4095:
4068:
4032:
4012:
3976:
3956:
3944:{\displaystyle O(K)}
3926:
3906:
3848:
3825:
3805:
3785:
3765:
3745:
3725:
3705:
3581:
3481:
3393:
3370:
3344:
3113:
3083:
3056:
2647:
2605:
2522:
2485:
2402:
2346:
2273:
2217:
2202:probability of word
2144:
2113:
2083:
2031:
2000:
1970:
1918:
1861:
1834:
1809:
1758:
1750:number of documents
1729:
1700:
1671:
1624:
1604:
1577:
1485:
1420:
1366:
1322:
1296:
1289:typically is sparse
1273:
1229:
1184:
1155:
1135:
1094:
1050:
1005:
975:
955:
935:
880:
834:
814:
794:
774:
754:
731:
711:
663:
643:
633:observable variables
605:
563:
524:
492:
460:
419:
16815:Interactive fiction
16745:Pachinko allocation
16702:Speech segmentation
16658:Google Ngram Viewer
16430:Machine translation
16420:Text simplification
16415:Sentence extraction
16303:Semantic similarity
16071:Topic Modeling Tool
15975:footnote references
15544:2004PNAS..101.5228G
15133:Pachinko allocation
14884:
14800:
14731:
14652:
14568:
14499:
14425:
14328:
14241:
14149:
14065:
13996:
13922:
13825:
13748:
13671:
13574:
13481:
13372:
13269:
13186:
13109:
13012:
12919:
12754:
12716:
12626:
12550:
12473:
12386:
12310:
12230:
12154:
12064:
11988:
11908:
11832:
11755:
11555:
11479:
10218:
10144:
9950:
9876:
9574:
9500:
9303:
9282:
9061:
9059:
8995:
8332:
8258:
8084:
8063:
7980:
7906:
7809:
7735:
7561:
7540:
7199:
7178:
7095:
7021:
6932:. According to the
6897:
6876:
6686:
6684:
6620:
6403:
6401:
6195:
6125:
5997:
5880:
3675:
3652:
3628:
3551:
3528:
3451:
3359:{\displaystyle a,b}
3314:
3291:
3267:
3234:
3211:
3178:
2206:occurring in topic
1653:
1562:here refers to the
1416:(a) Choose a topic
1311:{\displaystyle i,j}
172:association studies
136:LDA was applied in
119:population genetics
101:(and, therefore, a
16825:Question answering
16697:Speech recognition
16562:Corpus linguistics
16542:Language resources
16325:Textual entailment
16308:Sentiment analysis
15798:Jordan, Michael I.
15091:
15071:
15036:
15001:
14905:
14903:
14837:
14753:
14684:
14605:
14521:
14452:
14378:
14281:
14269:
14194:
14185:
14102:
14018:
13949:
13875:
13778:
13701:
13624:
13527:
13515:
13434:
13425:
13325:
13222:
13139:
13062:
12965:
12953:
12872:
12863:
12818:
12794:
12755:
12734:
12717:
12675:
12655:
12653:
12600:
12524:
12447:
12360:
12284:
12204:
12128:
12038:
11962:
11882:
11806:
11729:
11720:
11529:
11453:
11420:
11078:
11036:
10937:
10898:
10859:
10839:
10795:
10765:
10745:word token in the
10735:
10705:
10685:
10643:
10391:
10340:
10297:
10243:
10192:
10118:
9924:
9850:
9650:
9628:
9603:
9601:
9548:
9474:
9256:
9240:
9033:
9017:
8962:
8430:
8408:
8386:
8361:
8359:
8306:
8232:
8037:
8021:
7954:
7880:
7783:
7709:
7514:
7498:
7220:
7152:
7136:
7069:
6995:
6915:
6850:
6834:
6658:
6642:
6587:
6437:
6407:
6375:
6359:
6256:
6226:
6196:
6169:
6152:
6126:
6105:
6088:
6058:
6028:
5998:
5977:
5957:
5847:
5579:
5546:
5418:as the following:
5408:
5385:
5099:
5079:
5059:
5039:
5019:
4996:
4994:
4544:
4522:
4497:
4216:
4192:
4172:
4140:
4108:
4081:
4054:
4018:
3998:
3962:
3941:
3912:
3892:
3831:
3811:
3791:
3771:
3751:
3731:
3711:
3688:
3658:
3638:
3614:
3564:
3534:
3514:
3464:
3434:
3376:
3356:
3327:
3297:
3277:
3253:
3217:
3197:
3161:
3096:
3069:
2960:
2958:
2613:
2572:
2493:
2452:
2373:
2316:
2244:
2187:
2126:
2109:collection of all
2091:
2056:
2013:
1996:collection of all
1978:
1943:
1901:
1847:
1815:
1783:
1735:
1706:
1677:
1651:
1630:
1610:
1590:
1545:
1481:(b) Choose a word
1467:
1403:
1352:
1308:
1279:
1259:
1215:
1167:
1141:
1117:
1080:
1036:
988:
961:
941:
926:Generative process
912:
866:
820:
800:
780:
760:
737:
717:
695:
649:
621:
599:
579:
540:
505:
473:
432:
388:
351:K-means clustering
310:Certain terms are
215:, a subproblem in
195:In the context of
117:In the context of
16874:
16873:
16830:Virtual assistant
16755:Computer-assisted
16681:
16680:
16438:Computer-assisted
16396:
16395:
16388:Word segmentation
16350:Text segmentation
16288:Semantic analysis
16276:Syntactic parsing
16261:Ontology learning
16003:
16002:
15995:
15856:Molecular Ecology
15475:10.5334/tismir.46
15448:Rohrmeier, Martin
15286:Jordan, Michael I
15094:{\displaystyle d}
15004:{\displaystyle d}
14899:
14667:
14445:
14260:
14176:
14164:
13942:
13691:
13500:
13410:
13398:
13129:
12938:
12848:
12821:{\displaystyle k}
12646:
12406:
12250:
12084:
11928:
11705:
11672:
11575:
11405:
11393:
10862:{\displaystyle Z}
10708:{\displaystyle Z}
10638:
10238:
10086:
9970:
9818:
9594:
9442:
9217:
8939:
8352:
8200:
7998:
7829:
7677:
7475:
7113:
6811:
6564:
5824:
4021:{\displaystyle C}
3965:{\displaystyle B}
3915:{\displaystyle A}
3794:{\displaystyle A}
3774:{\displaystyle w}
3754:{\displaystyle C}
3734:{\displaystyle d}
3714:{\displaystyle B}
3686:
3562:
3462:
3379:{\displaystyle c}
3325:
3245:
3189:
3010:variational Bayes
3004:Variational Bayes
2638:
2637:
2590:identity of word
1818:{\displaystyle N}
1738:{\displaystyle M}
1709:{\displaystyle V}
1680:{\displaystyle K}
1633:{\displaystyle z}
1613:{\displaystyle w}
964:{\displaystyle M}
944:{\displaystyle D}
740:{\displaystyle V}
720:{\displaystyle V}
652:{\displaystyle K}
150:Michael I. Jordan
79:
78:
71:
16:Probability model
16904:
16851:Formal semantics
16800:Natural language
16707:Speech synthesis
16689:and data capture
16592:Semantic network
16567:Lexical resource
16550:
16549:
16368:Lexical analysis
16346:
16345:
16271:Semantic parsing
16140:
16133:
16126:
16117:
16116:
15998:
15991:
15987:
15984:
15978:
15946:
15945:
15938:
15927:
15926:
15920:
15911:
15905:
15904:
15892:
15886:
15885:
15875:
15847:
15841:
15840:
15838:
15827:
15821:
15820:
15808:
15796:Blei, David M.;
15793:
15787:
15786:
15776:
15767:
15761:
15760:
15732:
15726:
15725:
15717:
15711:
15710:
15698:
15687:
15681:
15680:
15670:
15634:
15625:
15624:
15614:
15597:(9): 1655–1664.
15582:
15576:
15575:
15565:
15555:
15523:
15517:
15516:
15504:
15494:
15488:
15487:
15477:
15450:(October 2020).
15443:
15437:
15436:
15418:
15408:
15384:
15378:
15377:
15367:
15333:
15327:
15326:
15324:
15323:
15314:. Archived from
15281:
15270:
15269:
15259:
15223:
15217:
15216:
15206:
15162:
15100:
15098:
15097:
15092:
15080:
15078:
15077:
15072:
15045:
15043:
15042:
15037:
15010:
15008:
15007:
15002:
14934:Related problems
14928:Bayesian network
14926:priors out of a
14914:
14912:
14911:
14906:
14904:
14900:
14898:
14897:
14896:
14883:
14857:
14835:
14830:
14814:
14813:
14812:
14799:
14773:
14751:
14749:
14745:
14744:
14743:
14730:
14704:
14672:
14668:
14666:
14665:
14664:
14651:
14625:
14603:
14598:
14582:
14581:
14580:
14567:
14541:
14519:
14517:
14513:
14512:
14511:
14498:
14472:
14446:
14444:
14443:
14439:
14438:
14437:
14424:
14398:
14376:
14371:
14347:
14346:
14342:
14341:
14340:
14327:
14301:
14271:
14268:
14259:
14255:
14254:
14253:
14240:
14214:
14184:
14169:
14165:
14163:
14162:
14161:
14148:
14122:
14100:
14095:
14079:
14078:
14077:
14064:
14038:
14016:
14014:
14010:
14009:
14008:
13995:
13969:
13943:
13941:
13940:
13936:
13935:
13934:
13921:
13895:
13873:
13868:
13844:
13843:
13839:
13838:
13837:
13824:
13798:
13768:
13766:
13762:
13761:
13760:
13747:
13721:
13692:
13690:
13689:
13685:
13684:
13683:
13670:
13644:
13622:
13617:
13593:
13592:
13588:
13587:
13586:
13573:
13547:
13517:
13514:
13499:
13495:
13494:
13493:
13480:
13454:
13424:
13403:
13399:
13397:
13396:
13392:
13385:
13384:
13371:
13345:
13323:
13318:
13294:
13293:
13289:
13282:
13281:
13268:
13242:
13212:
13210:
13206:
13199:
13198:
13185:
13159:
13130:
13128:
13127:
13123:
13122:
13121:
13108:
13082:
13060:
13055:
13031:
13030:
13026:
13025:
13024:
13011:
12985:
12955:
12952:
12937:
12933:
12932:
12931:
12918:
12892:
12862:
12841:
12827:
12825:
12824:
12819:
12803:
12801:
12800:
12795:
12793:
12792:
12764:
12762:
12761:
12756:
12753:
12748:
12726:
12724:
12723:
12718:
12715:
12689:
12664:
12662:
12661:
12656:
12654:
12647:
12645:
12644:
12640:
12639:
12638:
12625:
12620:
12598:
12593:
12569:
12568:
12564:
12563:
12562:
12549:
12544:
12514:
12511:
12506:
12491:
12487:
12486:
12485:
12472:
12467:
12437:
12432:
12411:
12407:
12405:
12404:
12400:
12399:
12398:
12385:
12380:
12358:
12353:
12329:
12328:
12324:
12323:
12322:
12309:
12304:
12274:
12271:
12266:
12251:
12249:
12248:
12244:
12243:
12242:
12229:
12224:
12202:
12197:
12173:
12172:
12168:
12167:
12166:
12153:
12148:
12118:
12113:
12097:
12089:
12085:
12083:
12082:
12078:
12077:
12076:
12063:
12058:
12036:
12031:
12007:
12006:
12002:
12001:
12000:
11987:
11982:
11952:
11949:
11944:
11929:
11927:
11926:
11922:
11921:
11920:
11907:
11902:
11880:
11875:
11851:
11850:
11846:
11845:
11844:
11831:
11826:
11796:
11791:
11775:
11773:
11769:
11768:
11767:
11754:
11749:
11719:
11703:
11698:
11683:
11682:
11677:
11673:
11671:
11667:
11666:
11650:
11645:
11629:
11628:
11624:
11623:
11622:
11612:
11607:
11583:
11576:
11574:
11573:
11569:
11568:
11567:
11554:
11549:
11527:
11522:
11498:
11497:
11493:
11492:
11491:
11478:
11473:
11443:
11438:
11422:
11419:
11404:
11403:
11398:
11394:
11392:
11388:
11387:
11371:
11366:
11350:
11349:
11345:
11344:
11343:
11333:
11328:
11304:
11291:
11272:
11264:
11263:
11262:
11228:
11227:
11194:
11175:
11167:
11166:
11165:
11131:
11130:
11087:
11085:
11084:
11079:
11077:
11076:
11045:
11043:
11042:
11037:
11035:
11031:
11018:
11010:
11009:
11008:
10980:
10979:
10946:
10944:
10943:
10938:
10936:
10935:
10907:
10905:
10904:
10899:
10897:
10896:
10868:
10866:
10865:
10860:
10849:denotes all the
10848:
10846:
10845:
10840:
10838:
10837:
10836:
10804:
10802:
10801:
10796:
10794:
10793:
10774:
10772:
10771:
10766:
10764:
10763:
10744:
10742:
10741:
10736:
10734:
10733:
10714:
10712:
10711:
10706:
10694:
10692:
10691:
10686:
10684:
10683:
10652:
10650:
10649:
10644:
10639:
10637:
10621:
10613:
10612:
10611:
10579:
10563:
10555:
10554:
10553:
10525:
10524:
10496:
10476:
10468:
10467:
10466:
10438:
10437:
10400:
10398:
10397:
10392:
10375:
10367:
10349:
10347:
10346:
10341:
10324:
10306:
10304:
10303:
10298:
10281:
10273:
10252:
10250:
10249:
10244:
10239:
10237:
10236:
10232:
10231:
10230:
10217:
10212:
10190:
10185:
10161:
10157:
10156:
10143:
10138:
10110:
10105:
10089:
10087:
10085:
10081:
10080:
10064:
10059:
10043:
10042:
10038:
10037:
10036:
10026:
10021:
9997:
9994:
9989:
9971:
9969:
9968:
9964:
9963:
9962:
9949:
9944:
9922:
9917:
9893:
9889:
9888:
9875:
9870:
9842:
9837:
9821:
9819:
9817:
9813:
9812:
9796:
9791:
9775:
9774:
9770:
9769:
9768:
9758:
9753:
9729:
9726:
9721:
9688:
9680:
9660:integrated out:
9659:
9657:
9656:
9651:
9649:
9637:
9635:
9634:
9629:
9627:
9612:
9610:
9609:
9604:
9602:
9595:
9593:
9592:
9588:
9587:
9586:
9573:
9568:
9546:
9541:
9517:
9513:
9512:
9499:
9494:
9466:
9461:
9445:
9443:
9441:
9437:
9436:
9420:
9415:
9399:
9398:
9394:
9393:
9392:
9382:
9377:
9353:
9350:
9345:
9326:
9317:
9316:
9302:
9295:
9294:
9281:
9276:
9254:
9238:
9233:
9218:
9216:
9212:
9211:
9195:
9190:
9174:
9173:
9169:
9168:
9167:
9157:
9152:
9128:
9126:
9125:
9124:
9123:
9108:
9103:
9084:
9075:
9074:
9060:
9058:
9053:
9031:
9015:
9010:
8994:
8987:
8986:
8976:
8960:
8955:
8940:
8938:
8934:
8933:
8917:
8912:
8896:
8895:
8891:
8890:
8889:
8879:
8874:
8850:
8848:
8847:
8846:
8845:
8830:
8825:
8806:
8797:
8796:
8780:
8779:
8778:
8777:
8754:
8753:
8731:
8726:
8710:
8705:
8681:
8680:
8665:
8664:
8663:
8662:
8647:
8642:
8623:
8614:
8602:
8601:
8600:
8599:
8576:
8575:
8553:
8548:
8532:
8527:
8503:
8502:
8486:
8481:
8466:
8465:
8453:
8439:
8437:
8436:
8431:
8429:
8417:
8415:
8414:
8409:
8407:
8395:
8393:
8392:
8387:
8385:
8370:
8368:
8367:
8362:
8360:
8353:
8351:
8350:
8346:
8345:
8344:
8331:
8326:
8304:
8299:
8275:
8271:
8270:
8257:
8252:
8224:
8219:
8203:
8201:
8199:
8195:
8194:
8178:
8173:
8157:
8156:
8152:
8151:
8150:
8140:
8135:
8111:
8107:
8098:
8097:
8083:
8076:
8075:
8062:
8057:
8035:
8019:
8014:
7999:
7997:
7993:
7992:
7979:
7974:
7946:
7941:
7925:
7924:
7920:
7919:
7918:
7905:
7900:
7878:
7873:
7849:
7847:
7846:
7845:
7844:
7830:
7828:
7827:
7823:
7822:
7821:
7808:
7803:
7781:
7776:
7752:
7748:
7747:
7734:
7729:
7701:
7696:
7680:
7678:
7676:
7672:
7671:
7655:
7650:
7634:
7633:
7629:
7628:
7627:
7617:
7612:
7588:
7584:
7575:
7574:
7560:
7553:
7552:
7539:
7534:
7512:
7496:
7491:
7476:
7474:
7470:
7469:
7453:
7448:
7432:
7431:
7427:
7426:
7425:
7415:
7410:
7386:
7384:
7383:
7382:
7381:
7364:
7363:
7347:
7346:
7334:
7333:
7311:
7306:
7282:
7281:
7266:
7265:
7264:
7263:
7246:
7229:
7227:
7226:
7221:
7213:
7212:
7198:
7191:
7190:
7177:
7172:
7150:
7134:
7129:
7114:
7112:
7108:
7107:
7094:
7089:
7061:
7056:
7040:
7039:
7035:
7034:
7033:
7020:
7015:
6993:
6988:
6964:
6962:
6961:
6960:
6959:
6924:
6922:
6921:
6916:
6911:
6910:
6896:
6889:
6888:
6875:
6870:
6848:
6832:
6827:
6812:
6810:
6806:
6805:
6789:
6784:
6768:
6767:
6763:
6762:
6761:
6751:
6746:
6722:
6720:
6719:
6718:
6717:
6700:
6699:
6685:
6683:
6678:
6656:
6640:
6635:
6619:
6612:
6611:
6601:
6585:
6580:
6565:
6563:
6559:
6558:
6542:
6537:
6521:
6520:
6516:
6515:
6514:
6504:
6499:
6475:
6473:
6472:
6471:
6470:
6446:
6444:
6443:
6438:
6436:
6435:
6416:
6414:
6413:
6408:
6402:
6400:
6395:
6373:
6357:
6352:
6331:
6330:
6318:
6317:
6295:
6290:
6265:
6263:
6262:
6257:
6255:
6254:
6235:
6233:
6232:
6227:
6225:
6224:
6205:
6203:
6202:
6197:
6194:
6189:
6161:
6159:
6158:
6153:
6135:
6133:
6132:
6127:
6124:
6119:
6097:
6095:
6094:
6089:
6087:
6086:
6067:
6065:
6064:
6059:
6057:
6056:
6037:
6035:
6034:
6029:
6027:
6026:
6007:
6005:
6004:
5999:
5996:
5991:
5966:
5964:
5963:
5958:
5953:
5952:
5936:
5935:
5923:
5922:
5900:
5895:
5879:
5872:
5871:
5861:
5845:
5840:
5825:
5823:
5819:
5818:
5802:
5797:
5781:
5780:
5776:
5775:
5774:
5764:
5759:
5735:
5733:
5732:
5731:
5730:
5713:
5712:
5696:
5695:
5683:
5682:
5660:
5655:
5631:
5630:
5615:
5614:
5613:
5612:
5588:
5586:
5585:
5580:
5578:
5577:
5555:
5553:
5552:
5547:
5542:
5541:
5525:
5524:
5512:
5511:
5489:
5484:
5460:
5459:
5444:
5443:
5442:
5441:
5417:
5415:
5414:
5409:
5394:
5392:
5391:
5386:
5381:
5380:
5364:
5363:
5351:
5350:
5328:
5323:
5299:
5298:
5283:
5282:
5281:
5280:
5265:
5260:
5242:
5230:
5229:
5217:
5216:
5194:
5189:
5165:
5164:
5148:
5143:
5128:
5127:
5108:
5106:
5105:
5100:
5088:
5086:
5085:
5080:
5068:
5066:
5065:
5060:
5048:
5046:
5045:
5040:
5028:
5026:
5025:
5020:
5005:
5003:
5002:
4997:
4995:
4988:
4976:
4975:
4963:
4962:
4940:
4935:
4911:
4910:
4894:
4889:
4874:
4873:
4864:
4852:
4851:
4850:
4849:
4826:
4825:
4803:
4798:
4782:
4777:
4753:
4752:
4736:
4731:
4716:
4715:
4702:
4693:
4684:
4660:
4652:
4644:
4636:
4625:
4624:
4615:
4614:
4587:
4579:
4567:
4553:
4551:
4550:
4545:
4543:
4531:
4529:
4528:
4523:
4521:
4506:
4504:
4503:
4498:
4490:
4489:
4488:
4487:
4464:
4463:
4439:
4438:
4426:
4425:
4403:
4398:
4374:
4373:
4357:
4352:
4328:
4327:
4311:
4306:
4273:
4265:
4257:
4249:
4225:
4223:
4222:
4217:
4215:
4214:
4201:
4199:
4198:
4193:
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637:latent variables
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290:would suggest a
254:would suggest a
240:golden retriever
211:- specifically,
209:machine learning
203:Machine learning
138:machine learning
99:Bayesian network
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16221:Argument mining
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15591:Genome Research
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6217:
6213:
6211:
6208:
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6190:
6173:
6167:
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6163:
6141:
6138:
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6120:
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6103:
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6099:
6079:
6075:
6073:
6070:
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6049:
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6043:
6040:
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6019:
6015:
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5992:
5981:
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5717:
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5645:
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5313:
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5261:
5250:
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5225:
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5119:
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5074:
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5054:
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4984:
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4319:
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4213:
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4187:
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3296:
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3196:
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2120:
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2110:
2086:
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2080:
2038:
2034:
2032:
2029:
2028:
2007:
2003:
2001:
1998:
1997:
1973:
1971:
1968:
1967:
1925:
1921:
1919:
1916:
1915:
1895:
1891:
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1862:
1859:
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1841:
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1810:
1807:
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1765:
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1730:
1727:
1726:
1701:
1698:
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1672:
1669:
1668:
1646:
1625:
1622:
1621:
1605:
1602:
1601:
1584:
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1578:
1575:
1574:
1525:
1521:
1520:
1516:
1492:
1488:
1486:
1483:
1482:
1455:
1451:
1427:
1423:
1421:
1418:
1417:
1394:
1390:
1367:
1364:
1363:
1323:
1320:
1319:
1297:
1294:
1293:
1274:
1271:
1270:
1230:
1227:
1226:
1191:
1187:
1185:
1182:
1181:
1156:
1153:
1152:
1136:
1133:
1132:
1097:
1095:
1092:
1091:
1051:
1048:
1047:
1012:
1008:
1006:
1003:
1002:
982:
978:
976:
973:
972:
956:
953:
952:
936:
933:
932:
928:
906:
902:
887:
883:
881:
878:
877:
860:
856:
841:
837:
835:
832:
831:
815:
812:
811:
795:
792:
791:
775:
772:
771:
755:
752:
751:
732:
729:
728:
712:
709:
708:
689:
685:
670:
666:
664:
661:
660:
644:
641:
640:
612:
608:
606:
603:
602:
570:
566:
564:
561:
560:
531:
527:
525:
522:
521:
499:
495:
493:
490:
489:
467:
463:
461:
458:
457:
426:
422:
420:
417:
416:
377:
338:methods and an
213:topic discovery
205:
193:
184:
163:
158:
123:J. K. Pritchard
115:
75:
64:
58:
55:
47:help improve it
44:
35:
31:
24:
17:
12:
11:
5:
16910:
16900:
16899:
16894:
16889:
16872:
16871:
16869:
16868:
16863:
16858:
16853:
16847:
16845:
16841:
16840:
16838:
16837:
16832:
16827:
16822:
16812:
16806:
16804:
16802:user interface
16796:
16795:
16793:
16792:
16787:
16782:
16777:
16772:
16767:
16761:
16759:
16751:
16750:
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16714:
16709:
16704:
16699:
16693:
16691:
16683:
16682:
16679:
16678:
16676:
16675:
16670:
16665:
16660:
16655:
16650:
16645:
16640:
16634:
16632:
16628:
16627:
16625:
16624:
16619:
16614:
16609:
16604:
16599:
16594:
16589:
16584:
16579:
16574:
16569:
16564:
16558:
16556:
16547:
16538:
16537:
16535:
16534:
16529:
16527:Word embedding
16524:
16519:
16514:
16507:Language model
16504:
16499:
16494:
16489:
16484:
16478:
16476:
16469:
16468:
16466:
16465:
16460:
16458:Transfer-based
16455:
16450:
16445:
16440:
16434:
16432:
16426:
16425:
16423:
16422:
16417:
16412:
16406:
16404:
16398:
16397:
16394:
16393:
16391:
16390:
16385:
16380:
16375:
16370:
16365:
16360:
16354:
16352:
16343:
16342:
16337:
16332:
16327:
16322:
16317:
16311:
16310:
16305:
16300:
16295:
16290:
16285:
16280:
16279:
16278:
16273:
16263:
16258:
16253:
16248:
16243:
16238:
16233:
16231:Concept mining
16228:
16223:
16217:
16215:
16209:
16208:
16206:
16205:
16200:
16195:
16190:
16185:
16184:
16183:
16178:
16168:
16163:
16157:
16155:
16151:
16150:
16143:
16142:
16135:
16128:
16120:
16114:
16113:
16103:
16093:
16087:
16073:
16064:
16050:
16040:
16034:
16024:
16014:
16001:
16000:
15955:external links
15950:
15948:
15941:
15935:
15934:External links
15932:
15929:
15928:
15906:
15887:
15842:
15822:
15815:
15788:
15762:
15743:(2): 477–505.
15737:Scientometrics
15727:
15712:
15705:
15682:
15626:
15577:
15518:
15511:
15489:
15438:
15379:
15350:(5): 956–966.
15328:
15271:
15218:
15153:
15152:
15150:
15147:
15146:
15145:
15140:
15135:
15130:
15123:
15120:
15106:
15105:Spatial models
15103:
15090:
15070:
15067:
15064:
15061:
15058:
15055:
15035:
15032:
15029:
15026:
15023:
15020:
15000:
14981:LDA-dual model
14940:
14939:Related models
14937:
14935:
14932:
14916:
14915:
14895:
14891:
14887:
14882:
14879:
14876:
14873:
14870:
14867:
14864:
14861:
14856:
14853:
14850:
14847:
14844:
14840:
14834:
14829:
14826:
14823:
14819:
14811:
14807:
14803:
14798:
14795:
14792:
14789:
14786:
14783:
14780:
14777:
14772:
14769:
14766:
14763:
14760:
14756:
14748:
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14734:
14729:
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14723:
14720:
14717:
14714:
14711:
14708:
14703:
14700:
14697:
14694:
14691:
14687:
14682:
14678:
14675:
14673:
14671:
14663:
14659:
14655:
14650:
14647:
14644:
14641:
14638:
14635:
14632:
14629:
14624:
14621:
14618:
14615:
14612:
14608:
14602:
14597:
14594:
14591:
14587:
14579:
14575:
14571:
14566:
14563:
14560:
14557:
14554:
14551:
14548:
14545:
14540:
14537:
14534:
14531:
14528:
14524:
14516:
14510:
14506:
14502:
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14494:
14491:
14488:
14485:
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14479:
14476:
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14465:
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14450:
14442:
14436:
14432:
14428:
14423:
14420:
14417:
14414:
14411:
14408:
14405:
14402:
14397:
14394:
14391:
14388:
14385:
14381:
14375:
14370:
14367:
14364:
14360:
14355:
14351:
14345:
14339:
14335:
14331:
14326:
14323:
14320:
14317:
14314:
14311:
14308:
14305:
14300:
14297:
14294:
14291:
14288:
14284:
14279:
14275:
14267:
14263:
14258:
14252:
14248:
14244:
14239:
14236:
14233:
14230:
14227:
14224:
14221:
14218:
14213:
14210:
14207:
14204:
14201:
14197:
14192:
14188:
14183:
14179:
14175:
14172:
14170:
14168:
14160:
14156:
14152:
14147:
14144:
14141:
14138:
14135:
14132:
14129:
14126:
14121:
14118:
14115:
14112:
14109:
14105:
14099:
14094:
14091:
14088:
14084:
14076:
14072:
14068:
14063:
14060:
14057:
14054:
14051:
14048:
14045:
14042:
14037:
14034:
14031:
14028:
14025:
14021:
14013:
14007:
14003:
13999:
13994:
13991:
13988:
13985:
13982:
13979:
13976:
13973:
13968:
13965:
13962:
13959:
13956:
13952:
13947:
13939:
13933:
13929:
13925:
13920:
13917:
13914:
13911:
13908:
13905:
13902:
13899:
13894:
13891:
13888:
13885:
13882:
13878:
13872:
13867:
13864:
13861:
13857:
13852:
13848:
13842:
13836:
13832:
13828:
13823:
13820:
13817:
13814:
13811:
13808:
13805:
13802:
13797:
13794:
13791:
13788:
13785:
13781:
13776:
13772:
13765:
13759:
13755:
13751:
13746:
13743:
13740:
13737:
13734:
13731:
13728:
13725:
13720:
13717:
13714:
13711:
13708:
13704:
13699:
13695:
13688:
13682:
13678:
13674:
13669:
13666:
13663:
13660:
13657:
13654:
13651:
13648:
13643:
13640:
13637:
13634:
13631:
13627:
13621:
13616:
13613:
13610:
13606:
13601:
13597:
13591:
13585:
13581:
13577:
13572:
13569:
13566:
13563:
13560:
13557:
13554:
13551:
13546:
13543:
13540:
13537:
13534:
13530:
13525:
13521:
13513:
13510:
13507:
13503:
13498:
13492:
13488:
13484:
13479:
13476:
13473:
13470:
13467:
13464:
13461:
13458:
13453:
13450:
13447:
13444:
13441:
13437:
13432:
13428:
13423:
13420:
13417:
13413:
13409:
13406:
13404:
13402:
13395:
13391:
13388:
13383:
13379:
13375:
13370:
13367:
13364:
13361:
13358:
13355:
13352:
13349:
13344:
13341:
13338:
13335:
13332:
13328:
13322:
13317:
13314:
13311:
13307:
13302:
13298:
13292:
13288:
13285:
13280:
13276:
13272:
13267:
13264:
13261:
13258:
13255:
13252:
13249:
13246:
13241:
13238:
13235:
13232:
13229:
13225:
13220:
13216:
13209:
13205:
13202:
13197:
13193:
13189:
13184:
13181:
13178:
13175:
13172:
13169:
13166:
13163:
13158:
13155:
13152:
13149:
13146:
13142:
13137:
13133:
13126:
13120:
13116:
13112:
13107:
13104:
13101:
13098:
13095:
13092:
13089:
13086:
13081:
13078:
13075:
13072:
13069:
13065:
13059:
13054:
13051:
13048:
13044:
13039:
13035:
13029:
13023:
13019:
13015:
13010:
13007:
13004:
13001:
12998:
12995:
12992:
12989:
12984:
12981:
12978:
12975:
12972:
12968:
12963:
12959:
12951:
12948:
12945:
12941:
12936:
12930:
12926:
12922:
12917:
12914:
12911:
12908:
12905:
12902:
12899:
12896:
12891:
12888:
12885:
12882:
12879:
12875:
12870:
12866:
12861:
12858:
12855:
12851:
12847:
12844:
12842:
12840:
12817:
12806:gamma function
12791:
12788:
12785:
12782:
12779:
12775:
12752:
12747:
12744:
12741:
12737:
12714:
12711:
12708:
12705:
12702:
12699:
12696:
12693:
12688:
12685:
12682:
12678:
12666:
12665:
12650:
12643:
12637:
12633:
12629:
12624:
12619:
12616:
12613:
12610:
12607:
12603:
12597:
12592:
12589:
12586:
12582:
12577:
12573:
12567:
12561:
12557:
12553:
12548:
12543:
12540:
12537:
12534:
12531:
12527:
12522:
12518:
12510:
12505:
12502:
12499:
12495:
12490:
12484:
12480:
12476:
12471:
12466:
12463:
12460:
12457:
12454:
12450:
12445:
12441:
12436:
12431:
12428:
12425:
12421:
12417:
12414:
12412:
12410:
12403:
12397:
12393:
12389:
12384:
12379:
12376:
12373:
12370:
12367:
12363:
12357:
12352:
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4797:
4794:
4791:
4787:
4781:
4776:
4773:
4770:
4766:
4762:
4759:
4756:
4751:
4747:
4743:
4740:
4735:
4730:
4727:
4724:
4720:
4714:
4710:
4706:
4704:
4700:
4697:
4696:
4692:
4688:
4683:
4679:
4675:
4672:
4669:
4666:
4663:
4659:
4655:
4651:
4647:
4643:
4639:
4635:
4631:
4628:
4623:
4619:
4613:
4609:
4605:
4602:
4599:
4596:
4593:
4590:
4586:
4582:
4578:
4574:
4571:
4568:
4566:
4542:
4520:
4508:
4507:
4496:
4493:
4486:
4483:
4480:
4476:
4471:
4467:
4462:
4459:
4456:
4452:
4448:
4445:
4442:
4437:
4433:
4429:
4424:
4421:
4418:
4414:
4410:
4407:
4402:
4397:
4394:
4391:
4387:
4383:
4380:
4377:
4372:
4368:
4364:
4361:
4356:
4351:
4348:
4345:
4341:
4337:
4334:
4331:
4326:
4322:
4318:
4315:
4310:
4305:
4302:
4299:
4295:
4291:
4288:
4285:
4282:
4279:
4276:
4272:
4268:
4264:
4260:
4256:
4252:
4248:
4244:
4241:
4212:
4191:
4171:
4162:, which means
4155:
4152:
4139:
4136:
4133:
4130:
4105:
4101:
4078:
4074:
4053:
4048:
4044:
4040:
4037:
4017:
3997:
3992:
3988:
3984:
3981:
3961:
3940:
3937:
3934:
3931:
3911:
3891:
3888:
3885:
3882:
3879:
3876:
3873:
3869:
3865:
3862:
3859:
3856:
3853:
3830:
3810:
3790:
3770:
3750:
3730:
3710:
3699:
3698:
3684:
3681:
3678:
3673:
3670:
3665:
3661:
3655:
3650:
3645:
3641:
3637:
3634:
3631:
3626:
3621:
3617:
3608:
3603:
3600:
3597:
3593:
3589:
3586:
3575:
3574:
3560:
3557:
3554:
3549:
3546:
3541:
3537:
3531:
3526:
3521:
3517:
3508:
3503:
3500:
3497:
3493:
3489:
3486:
3475:
3474:
3460:
3457:
3454:
3449:
3446:
3441:
3437:
3431:
3428:
3420:
3415:
3412:
3409:
3405:
3401:
3398:
3375:
3355:
3352:
3349:
3338:
3337:
3323:
3320:
3317:
3312:
3309:
3304:
3300:
3294:
3289:
3284:
3280:
3276:
3273:
3270:
3265:
3260:
3256:
3249:
3243:
3240:
3237:
3232:
3229:
3224:
3220:
3214:
3209:
3204:
3200:
3193:
3187:
3184:
3181:
3176:
3173:
3168:
3164:
3158:
3155:
3149:
3146:
3143:
3140:
3135:
3132:
3129:
3125:
3121:
3118:
3093:
3089:
3066:
3062:
3041:
3038:
3029:
3026:
3021:
3018:
3005:
3002:
2998:Gibbs sampling
2993:
2990:
2975:
2972:
2971:
2970:
2955:
2948:
2945:
2941:
2935:
2930:
2927:
2922:
2918:
2914:
2911:
2909:
2903:
2899:
2895:
2892:
2889:
2886:
2883:
2880:
2877:
2874:
2871:
2868:
2864:
2860:
2859:
2856:
2851:
2846:
2841:
2838:
2833:
2829:
2825:
2822:
2820:
2814:
2810:
2806:
2803:
2800:
2797:
2794:
2791:
2788:
2785:
2782:
2779:
2775:
2771:
2770:
2767:
2763:
2759:
2756:
2751:
2747:
2743:
2740:
2738:
2734:
2731:
2728:
2725:
2722:
2717:
2712:
2711:
2708:
2704:
2700:
2697:
2692:
2688:
2684:
2681:
2679:
2675:
2672:
2669:
2666:
2663:
2658:
2653:
2652:
2636:
2635:
2632:
2623:
2611:
2599:
2598:
2588:
2582:
2567:
2563:
2559:
2556:
2553:
2550:
2547:
2544:
2541:
2538:
2535:
2532:
2528:
2516:
2515:
2512:
2503:
2491:
2479:
2478:
2468:
2462:
2447:
2443:
2439:
2436:
2433:
2430:
2427:
2424:
2421:
2418:
2415:
2412:
2408:
2396:
2395:
2389:
2383:
2370:
2367:
2364:
2361:
2358:
2353:
2340:
2339:
2329:
2326:
2313:
2310:
2307:
2304:
2301:
2298:
2295:
2292:
2289:
2286:
2283:
2279:
2267:
2266:
2260:
2254:
2241:
2238:
2235:
2232:
2229:
2224:
2211:
2210:
2200:
2197:
2184:
2181:
2178:
2175:
2172:
2169:
2166:
2163:
2160:
2157:
2154:
2150:
2138:
2137:
2123:
2119:
2107:
2101:
2089:
2077:
2076:
2069:
2066:
2053:
2050:
2047:
2044:
2041:
2037:
2025:
2024:
2010:
2006:
1994:
1988:
1976:
1964:
1963:
1956:
1953:
1940:
1937:
1934:
1931:
1928:
1924:
1912:
1911:
1898:
1894:
1888:
1883:
1880:
1877:
1873:
1869:
1866:
1844:
1840:
1828:
1825:
1814:
1803:
1802:
1796:
1793:
1780:
1777:
1774:
1771:
1768:
1764:
1752:
1751:
1748:
1745:
1734:
1723:
1722:
1719:
1716:
1705:
1694:
1693:
1690:
1687:
1676:
1665:
1664:
1661:
1658:
1645:
1642:
1629:
1609:
1587:
1583:
1556:
1555:
1544:
1541:
1534:
1531:
1528:
1524:
1519:
1515:
1512:
1509:
1506:
1501:
1498:
1495:
1491:
1478:
1477:
1466:
1463:
1458:
1454:
1450:
1447:
1444:
1441:
1436:
1433:
1430:
1426:
1402:
1397:
1393:
1389:
1386:
1383:
1380:
1377:
1374:
1371:
1351:
1348:
1345:
1342:
1339:
1336:
1333:
1330:
1327:
1307:
1304:
1301:
1278:
1258:
1255:
1252:
1249:
1246:
1243:
1240:
1237:
1234:
1214:
1211:
1208:
1205:
1202:
1199:
1194:
1190:
1166:
1163:
1160:
1140:
1116:
1113:
1110:
1106:
1103:
1100:
1079:
1076:
1073:
1070:
1067:
1064:
1061:
1058:
1055:
1035:
1032:
1029:
1026:
1023:
1020:
1015:
1011:
985:
981:
960:
951:consisting of
940:
927:
924:
909:
905:
901:
898:
895:
890:
886:
863:
859:
855:
852:
849:
844:
840:
819:
799:
779:
759:
736:
716:
692:
688:
684:
681:
678:
673:
669:
648:
618:
615:
611:
591:
590:
576:
573:
569:
558:
537:
534:
530:
519:
502:
498:
487:
470:
466:
455:
449:
443:
429:
425:
406:
392:plate notation
384:Plate notation
376:
373:
372:
371:
364:
361:
358:
332:
331:
328:
324:
308:
204:
201:
192:
189:
183:
180:
162:
159:
157:
154:
114:
111:
77:
76:
38:
36:
29:
15:
9:
6:
4:
3:
2:
16909:
16898:
16895:
16893:
16890:
16888:
16885:
16884:
16882:
16867:
16864:
16862:
16859:
16857:
16856:Hallucination
16854:
16852:
16849:
16848:
16846:
16842:
16836:
16833:
16831:
16828:
16826:
16823:
16820:
16816:
16813:
16811:
16808:
16807:
16805:
16803:
16797:
16791:
16790:Spell checker
16788:
16786:
16783:
16781:
16778:
16776:
16773:
16771:
16768:
16766:
16763:
16762:
16760:
16758:
16752:
16746:
16743:
16741:
16738:
16736:
16733:
16732:
16730:
16728:
16724:
16718:
16715:
16713:
16710:
16708:
16705:
16703:
16700:
16698:
16695:
16694:
16692:
16690:
16684:
16674:
16671:
16669:
16666:
16664:
16661:
16659:
16656:
16654:
16651:
16649:
16646:
16644:
16641:
16639:
16636:
16635:
16633:
16629:
16623:
16620:
16618:
16615:
16613:
16610:
16608:
16605:
16603:
16602:Speech corpus
16600:
16598:
16595:
16593:
16590:
16588:
16585:
16583:
16582:Parallel text
16580:
16578:
16575:
16573:
16570:
16568:
16565:
16563:
16560:
16559:
16557:
16551:
16548:
16543:
16539:
16533:
16530:
16528:
16525:
16523:
16520:
16518:
16515:
16512:
16508:
16505:
16503:
16500:
16498:
16495:
16493:
16490:
16488:
16485:
16483:
16480:
16479:
16477:
16474:
16470:
16464:
16461:
16459:
16456:
16454:
16451:
16449:
16446:
16444:
16443:Example-based
16441:
16439:
16436:
16435:
16433:
16431:
16427:
16421:
16418:
16416:
16413:
16411:
16408:
16407:
16405:
16403:
16399:
16389:
16386:
16384:
16381:
16379:
16376:
16374:
16373:Text chunking
16371:
16369:
16366:
16364:
16363:Lemmatisation
16361:
16359:
16356:
16355:
16353:
16351:
16347:
16341:
16338:
16336:
16333:
16331:
16328:
16326:
16323:
16321:
16318:
16316:
16313:
16312:
16309:
16306:
16304:
16301:
16299:
16296:
16294:
16291:
16289:
16286:
16284:
16281:
16277:
16274:
16272:
16269:
16268:
16267:
16264:
16262:
16259:
16257:
16254:
16252:
16249:
16247:
16244:
16242:
16239:
16237:
16234:
16232:
16229:
16227:
16224:
16222:
16219:
16218:
16216:
16214:
16213:Text analysis
16210:
16204:
16201:
16199:
16196:
16194:
16191:
16189:
16186:
16182:
16179:
16177:
16174:
16173:
16172:
16169:
16167:
16164:
16162:
16159:
16158:
16156:
16154:General terms
16152:
16148:
16141:
16136:
16134:
16129:
16127:
16122:
16121:
16118:
16111:
16107:
16104:
16101:
16097:
16094:
16091:
16088:
16085:
16081:
16077:
16076:LDA in Mahout
16074:
16072:
16068:
16065:
16062:
16058:
16054:
16051:
16048:
16044:
16041:
16038:
16035:
16033:
16029:
16025:
16022:
16018:
16015:
16012:
16008:
16005:
16004:
15997:
15994:
15986:
15976:
15972:
15971:inappropriate
15968:
15964:
15958:
15956:
15949:
15940:
15939:
15924:
15917:
15910:
15902:
15898:
15891:
15883:
15879:
15874:
15869:
15865:
15861:
15857:
15853:
15846:
15835:
15834:
15826:
15818:
15816:0-262-20152-6
15812:
15805:
15804:
15799:
15792:
15784:
15780:
15773:
15766:
15758:
15754:
15750:
15746:
15742:
15738:
15731:
15723:
15716:
15708:
15706:1-55860-897-4
15702:
15695:
15694:
15686:
15678:
15674:
15669:
15664:
15660:
15656:
15652:
15648:
15644:
15640:
15633:
15631:
15622:
15618:
15613:
15608:
15604:
15600:
15596:
15592:
15588:
15581:
15573:
15569:
15564:
15559:
15554:
15549:
15545:
15541:
15537:
15533:
15529:
15522:
15514:
15512:1-58113-646-3
15508:
15503:
15502:
15493:
15485:
15481:
15476:
15471:
15467:
15463:
15459:
15458:
15453:
15449:
15442:
15434:
15430:
15426:
15422:
15417:
15412:
15407:
15406:10.2196/48405
15402:
15399:(1): e48405.
15398:
15394:
15390:
15383:
15375:
15371:
15366:
15361:
15357:
15353:
15349:
15345:
15344:
15339:
15332:
15318:on 2012-05-01
15317:
15313:
15309:
15305:
15301:
15297:
15296:
15291:
15287:
15280:
15278:
15276:
15267:
15263:
15258:
15253:
15249:
15245:
15241:
15237:
15233:
15229:
15222:
15214:
15210:
15205:
15200:
15196:
15192:
15188:
15184:
15180:
15176:
15172:
15168:
15161:
15159:
15154:
15144:
15141:
15139:
15136:
15134:
15131:
15129:
15126:
15125:
15119:
15117:
15111:
15102:
15088:
15065:
15062:
15059:
15030:
15027:
15024:
14998:
14988:
14986:
14982:
14978:
14974:
14968:
14966:
14962:
14958:
14954:
14950:
14946:
14931:
14929:
14925:
14921:
14893:
14889:
14885:
14877:
14874:
14871:
14865:
14862:
14859:
14854:
14851:
14845:
14838:
14832:
14827:
14824:
14821:
14817:
14809:
14805:
14801:
14793:
14790:
14787:
14781:
14778:
14775:
14770:
14767:
14761:
14754:
14746:
14740:
14736:
14732:
14724:
14721:
14718:
14712:
14709:
14706:
14698:
14692:
14689:
14685:
14680:
14676:
14674:
14661:
14657:
14653:
14645:
14642:
14639:
14633:
14630:
14627:
14622:
14619:
14613:
14606:
14600:
14595:
14592:
14589:
14585:
14577:
14573:
14569:
14561:
14558:
14555:
14549:
14546:
14543:
14538:
14535:
14529:
14522:
14514:
14508:
14504:
14500:
14492:
14489:
14486:
14480:
14477:
14474:
14466:
14460:
14457:
14453:
14448:
14440:
14434:
14430:
14426:
14418:
14415:
14412:
14406:
14403:
14400:
14395:
14392:
14386:
14379:
14373:
14368:
14365:
14362:
14358:
14353:
14343:
14337:
14333:
14329:
14321:
14318:
14315:
14309:
14306:
14303:
14298:
14295:
14289:
14282:
14277:
14265:
14261:
14256:
14250:
14246:
14242:
14234:
14231:
14228:
14222:
14219:
14216:
14208:
14202:
14199:
14195:
14190:
14181:
14177:
14173:
14171:
14158:
14154:
14150:
14142:
14139:
14136:
14130:
14127:
14124:
14119:
14116:
14110:
14103:
14097:
14092:
14089:
14086:
14082:
14074:
14070:
14066:
14058:
14055:
14052:
14046:
14043:
14040:
14035:
14032:
14026:
14019:
14011:
14005:
14001:
13997:
13989:
13986:
13983:
13977:
13974:
13971:
13963:
13957:
13954:
13950:
13945:
13937:
13931:
13927:
13923:
13915:
13912:
13909:
13903:
13900:
13897:
13892:
13889:
13883:
13876:
13870:
13865:
13862:
13859:
13855:
13850:
13840:
13834:
13830:
13826:
13818:
13815:
13812:
13806:
13803:
13800:
13795:
13792:
13786:
13779:
13774:
13763:
13757:
13753:
13749:
13741:
13738:
13735:
13729:
13726:
13723:
13715:
13709:
13706:
13702:
13697:
13686:
13680:
13676:
13672:
13664:
13661:
13658:
13652:
13649:
13646:
13641:
13638:
13632:
13625:
13619:
13614:
13611:
13608:
13604:
13599:
13589:
13583:
13579:
13575:
13567:
13564:
13561:
13555:
13552:
13549:
13544:
13541:
13535:
13528:
13523:
13511:
13508:
13505:
13501:
13496:
13490:
13486:
13482:
13474:
13471:
13468:
13462:
13459:
13456:
13448:
13442:
13439:
13435:
13430:
13421:
13418:
13415:
13411:
13407:
13405:
13393:
13389:
13386:
13381:
13377:
13373:
13365:
13362:
13359:
13353:
13350:
13347:
13342:
13339:
13333:
13326:
13320:
13315:
13312:
13309:
13305:
13300:
13290:
13286:
13283:
13278:
13274:
13270:
13262:
13259:
13256:
13250:
13247:
13244:
13239:
13236:
13230:
13223:
13218:
13207:
13203:
13200:
13195:
13191:
13187:
13179:
13176:
13173:
13167:
13164:
13161:
13153:
13147:
13144:
13140:
13135:
13124:
13118:
13114:
13110:
13102:
13099:
13096:
13090:
13087:
13084:
13079:
13076:
13070:
13063:
13057:
13052:
13049:
13046:
13042:
13037:
13027:
13021:
13017:
13013:
13005:
13002:
12999:
12993:
12990:
12987:
12982:
12979:
12973:
12966:
12961:
12949:
12946:
12943:
12939:
12934:
12928:
12924:
12920:
12912:
12909:
12906:
12900:
12897:
12894:
12886:
12880:
12877:
12873:
12868:
12859:
12856:
12853:
12849:
12845:
12843:
12831:
12830:
12829:
12815:
12807:
12786:
12783:
12780:
12773:
12765:but with the
12750:
12745:
12742:
12739:
12735:
12709:
12706:
12703:
12697:
12694:
12691:
12686:
12683:
12680:
12676:
12668:Finally, let
12648:
12641:
12635:
12631:
12627:
12622:
12617:
12614:
12608:
12601:
12595:
12590:
12587:
12584:
12580:
12575:
12565:
12559:
12555:
12551:
12546:
12541:
12538:
12532:
12525:
12520:
12508:
12503:
12500:
12497:
12493:
12488:
12482:
12478:
12474:
12469:
12461:
12455:
12452:
12448:
12443:
12434:
12429:
12426:
12423:
12419:
12415:
12413:
12401:
12395:
12391:
12387:
12382:
12377:
12374:
12368:
12361:
12355:
12350:
12347:
12344:
12340:
12335:
12325:
12319:
12315:
12311:
12306:
12301:
12298:
12292:
12285:
12280:
12268:
12263:
12260:
12257:
12253:
12245:
12239:
12235:
12231:
12226:
12218:
12212:
12209:
12205:
12199:
12194:
12191:
12188:
12184:
12179:
12169:
12163:
12159:
12155:
12150:
12142:
12136:
12133:
12129:
12124:
12115:
12110:
12107:
12104:
12100:
12093:
12091:
12079:
12073:
12069:
12065:
12060:
12055:
12052:
12046:
12039:
12033:
12028:
12025:
12022:
12018:
12013:
12003:
11997:
11993:
11989:
11984:
11979:
11976:
11970:
11963:
11958:
11946:
11941:
11938:
11935:
11931:
11923:
11917:
11913:
11909:
11904:
11896:
11890:
11887:
11883:
11877:
11872:
11869:
11866:
11862:
11857:
11847:
11841:
11837:
11833:
11828:
11820:
11814:
11811:
11807:
11802:
11793:
11788:
11785:
11782:
11778:
11770:
11764:
11760:
11756:
11751:
11746:
11743:
11737:
11730:
11725:
11716:
11713:
11710:
11706:
11700:
11695:
11692:
11689:
11685:
11679:
11674:
11663:
11659:
11647:
11642:
11639:
11636:
11632:
11625:
11619:
11615:
11609:
11604:
11601:
11598:
11594:
11589:
11579:
11570:
11564:
11560:
11556:
11551:
11543:
11537:
11534:
11530:
11524:
11519:
11516:
11513:
11509:
11504:
11494:
11488:
11484:
11480:
11475:
11467:
11461:
11458:
11454:
11449:
11440:
11435:
11432:
11429:
11425:
11416:
11413:
11410:
11406:
11400:
11395:
11384:
11380:
11368:
11363:
11360:
11357:
11353:
11346:
11340:
11336:
11330:
11325:
11322:
11319:
11315:
11310:
11300:
11295:
11293:
11282:
11279:
11276:
11273:
11265:
11235:
11232:
11229:
11221:
11218:
11215:
11208:
11201:
11198:
11196:
11185:
11182:
11179:
11176:
11168:
11138:
11135:
11132:
11124:
11121:
11118:
11111:
11102:
11091:
11090:
11089:
11070:
11067:
11064:
11057:
11032:
11028:
11025:
11022:
11019:
11011:
10981:
10976:
10973:
10970:
10966:
10961:
10957:
10950:
10949:
10948:
10929:
10926:
10923:
10916:
10890:
10887:
10884:
10877:
10856:
10790:
10787:
10783:
10760:
10757:
10753:
10730:
10727:
10723:
10702:
10677:
10674:
10671:
10664:
10640:
10631:
10628:
10625:
10622:
10614:
10581:
10573:
10570:
10567:
10564:
10556:
10526:
10518:
10515:
10512:
10505:
10498:
10492:
10486:
10483:
10480:
10477:
10469:
10439:
10431:
10428:
10425:
10418:
10411:
10404:
10403:
10402:
10385:
10382:
10379:
10376:
10368:
10357:
10334:
10331:
10328:
10325:
10314:
10291:
10288:
10285:
10282:
10274:
10263:
10240:
10233:
10227:
10223:
10219:
10214:
10209:
10206:
10200:
10193:
10187:
10182:
10179:
10176:
10172:
10167:
10153:
10149:
10145:
10140:
10135:
10132:
10126:
10119:
10107:
10102:
10099:
10096:
10092:
10077:
10073:
10061:
10056:
10053:
10050:
10046:
10039:
10033:
10029:
10023:
10018:
10015:
10012:
10008:
10003:
9991:
9986:
9983:
9980:
9976:
9972:
9965:
9959:
9955:
9951:
9946:
9938:
9932:
9929:
9925:
9919:
9914:
9911:
9908:
9904:
9899:
9885:
9881:
9877:
9872:
9864:
9858:
9855:
9851:
9839:
9834:
9831:
9828:
9824:
9809:
9805:
9793:
9788:
9785:
9782:
9778:
9771:
9765:
9761:
9755:
9750:
9747:
9744:
9740:
9735:
9723:
9718:
9715:
9712:
9708:
9704:
9698:
9695:
9692:
9689:
9681:
9670:
9663:
9662:
9661:
9596:
9589:
9583:
9579:
9575:
9570:
9565:
9562:
9556:
9549:
9543:
9538:
9535:
9532:
9528:
9523:
9509:
9505:
9501:
9496:
9491:
9488:
9482:
9475:
9463:
9458:
9455:
9452:
9448:
9433:
9429:
9417:
9412:
9409:
9406:
9402:
9395:
9389:
9385:
9379:
9374:
9371:
9368:
9364:
9359:
9347:
9342:
9339:
9336:
9332:
9329:
9322:
9313:
9309:
9305:
9299:
9296:
9291:
9287:
9283:
9278:
9273:
9270:
9264:
9257:
9251:
9248:
9245:
9241:
9235:
9230:
9227:
9224:
9220:
9208:
9204:
9192:
9187:
9184:
9181:
9177:
9170:
9164:
9160:
9154:
9149:
9146:
9143:
9139:
9134:
9120:
9116:
9111:
9105:
9100:
9097:
9094:
9090:
9087:
9080:
9071:
9067:
9063:
9055:
9050:
9047:
9041:
9034:
9028:
9025:
9022:
9018:
9012:
9007:
9004:
9001:
8997:
8991:
8988:
8983:
8979:
8973:
8970:
8967:
8963:
8957:
8952:
8949:
8946:
8942:
8930:
8926:
8914:
8909:
8906:
8903:
8899:
8892:
8886:
8882:
8876:
8871:
8868:
8865:
8861:
8856:
8842:
8838:
8833:
8827:
8822:
8819:
8816:
8812:
8809:
8802:
8793:
8789:
8785:
8774:
8771:
8768:
8764:
8759:
8755:
8750:
8747:
8744:
8740:
8733:
8728:
8723:
8720:
8717:
8713:
8707:
8702:
8699:
8696:
8692:
8685:
8682:
8677:
8673:
8666:
8659:
8655:
8650:
8644:
8639:
8636:
8633:
8629:
8626:
8619:
8607:
8596:
8593:
8590:
8586:
8581:
8577:
8572:
8569:
8566:
8562:
8555:
8550:
8545:
8542:
8539:
8535:
8529:
8524:
8521:
8518:
8514:
8507:
8504:
8499:
8495:
8488:
8483:
8478:
8475:
8472:
8468:
8458:
8455:
8443:
8442:
8441:
8354:
8347:
8341:
8337:
8333:
8328:
8320:
8314:
8311:
8307:
8301:
8296:
8293:
8290:
8286:
8281:
8267:
8263:
8259:
8254:
8246:
8240:
8237:
8233:
8221:
8216:
8213:
8210:
8206:
8191:
8187:
8175:
8170:
8167:
8164:
8160:
8153:
8147:
8143:
8137:
8132:
8129:
8126:
8122:
8117:
8103:
8094:
8090:
8086:
8080:
8077:
8072:
8068:
8064:
8059:
8051:
8045:
8042:
8038:
8032:
8029:
8026:
8022:
8016:
8011:
8008:
8005:
8001:
7989:
7985:
7981:
7976:
7968:
7962:
7959:
7955:
7943:
7938:
7935:
7932:
7928:
7921:
7915:
7911:
7907:
7902:
7894:
7888:
7885:
7881:
7875:
7870:
7867:
7864:
7860:
7855:
7841:
7837:
7832:
7824:
7818:
7814:
7810:
7805:
7797:
7791:
7788:
7784:
7778:
7773:
7770:
7767:
7763:
7758:
7744:
7740:
7736:
7731:
7723:
7717:
7714:
7710:
7698:
7693:
7690:
7687:
7683:
7668:
7664:
7652:
7647:
7644:
7641:
7637:
7630:
7624:
7620:
7614:
7609:
7606:
7603:
7599:
7594:
7580:
7571:
7567:
7563:
7557:
7554:
7549:
7545:
7541:
7536:
7528:
7522:
7519:
7515:
7509:
7506:
7503:
7499:
7493:
7488:
7485:
7482:
7478:
7466:
7462:
7450:
7445:
7442:
7439:
7435:
7428:
7422:
7418:
7412:
7407:
7404:
7401:
7397:
7392:
7378:
7374:
7369:
7365:
7360:
7356:
7352:
7343:
7339:
7335:
7330:
7327:
7324:
7320:
7313:
7308:
7303:
7300:
7297:
7293:
7286:
7283:
7278:
7274:
7267:
7260:
7256:
7251:
7248:
7236:
7235:
7234:
7217:
7214:
7209:
7205:
7201:
7195:
7192:
7187:
7183:
7179:
7174:
7166:
7160:
7157:
7153:
7147:
7144:
7141:
7137:
7131:
7126:
7123:
7120:
7116:
7104:
7100:
7096:
7091:
7083:
7077:
7074:
7070:
7058:
7053:
7050:
7047:
7043:
7036:
7030:
7026:
7022:
7017:
7009:
7003:
7000:
6996:
6990:
6985:
6982:
6979:
6975:
6970:
6956:
6952:
6947:
6939:
6938:
6937:
6935:
6931:
6912:
6907:
6903:
6899:
6893:
6890:
6885:
6881:
6877:
6872:
6864:
6858:
6855:
6851:
6845:
6842:
6839:
6835:
6829:
6824:
6821:
6818:
6814:
6802:
6798:
6786:
6781:
6778:
6775:
6771:
6764:
6758:
6754:
6748:
6743:
6740:
6737:
6733:
6728:
6714:
6710:
6705:
6701:
6696:
6692:
6688:
6680:
6672:
6666:
6663:
6659:
6653:
6650:
6647:
6643:
6637:
6632:
6629:
6626:
6622:
6616:
6613:
6608:
6604:
6598:
6595:
6592:
6588:
6582:
6577:
6574:
6571:
6567:
6555:
6551:
6539:
6534:
6531:
6528:
6524:
6517:
6511:
6507:
6501:
6496:
6493:
6490:
6486:
6481:
6467:
6463:
6458:
6450:
6449:
6448:
6432:
6428:
6404:
6397:
6389:
6383:
6380:
6376:
6370:
6367:
6364:
6360:
6354:
6349:
6346:
6343:
6339:
6335:
6327:
6323:
6319:
6314:
6311:
6308:
6304:
6297:
6292:
6287:
6284:
6281:
6277:
6269:
6268:
6267:
6251:
6248:
6244:
6221:
6218:
6214:
6191:
6183:
6177:
6174:
6170:
6146:
6121:
6116:
6113:
6110:
6106:
6083:
6080:
6076:
6053:
6050:
6046:
6023:
6020:
6016:
5993:
5988:
5985:
5982:
5978:
5954:
5949:
5945:
5941:
5932:
5928:
5924:
5919:
5916:
5913:
5909:
5902:
5897:
5892:
5889:
5886:
5882:
5876:
5873:
5868:
5864:
5858:
5855:
5852:
5848:
5842:
5837:
5834:
5831:
5827:
5815:
5811:
5799:
5794:
5791:
5788:
5784:
5777:
5771:
5767:
5761:
5756:
5753:
5750:
5746:
5741:
5727:
5723:
5718:
5714:
5709:
5705:
5701:
5692:
5688:
5684:
5679:
5676:
5673:
5669:
5662:
5657:
5652:
5649:
5646:
5642:
5635:
5632:
5627:
5623:
5616:
5609:
5605:
5600:
5592:
5591:
5590:
5574:
5571:
5567:
5543:
5538:
5534:
5530:
5521:
5517:
5513:
5508:
5505:
5502:
5498:
5491:
5486:
5481:
5478:
5475:
5471:
5464:
5461:
5456:
5452:
5445:
5438:
5434:
5429:
5421:
5420:
5419:
5405:
5382:
5377:
5373:
5369:
5360:
5356:
5352:
5347:
5344:
5341:
5337:
5330:
5325:
5320:
5317:
5314:
5310:
5303:
5300:
5295:
5291:
5284:
5277:
5273:
5268:
5262:
5257:
5254:
5251:
5247:
5243:
5235:
5226:
5222:
5218:
5213:
5210:
5207:
5203:
5196:
5191:
5186:
5183:
5180:
5176:
5169:
5166:
5161:
5157:
5150:
5145:
5140:
5137:
5134:
5130:
5120:
5112:
5111:
5110:
5096:
5076:
5056:
5036:
5016:
4989:
4981:
4972:
4968:
4964:
4959:
4956:
4953:
4949:
4942:
4937:
4932:
4929:
4926:
4922:
4915:
4912:
4907:
4903:
4896:
4891:
4886:
4883:
4880:
4876:
4866:
4857:
4846:
4843:
4840:
4836:
4831:
4827:
4822:
4819:
4816:
4812:
4805:
4800:
4795:
4792:
4789:
4785:
4779:
4774:
4771:
4768:
4764:
4757:
4754:
4749:
4745:
4738:
4733:
4728:
4725:
4722:
4718:
4708:
4705:
4698:
4686:
4677:
4670:
4667:
4664:
4661:
4653:
4645:
4637:
4626:
4617:
4607:
4603:
4597:
4594:
4591:
4588:
4580:
4569:
4557:
4556:
4555:
4494:
4484:
4481:
4478:
4474:
4469:
4465:
4460:
4457:
4454:
4450:
4443:
4435:
4431:
4427:
4422:
4419:
4416:
4412:
4405:
4400:
4395:
4392:
4389:
4385:
4378:
4375:
4370:
4366:
4359:
4354:
4349:
4346:
4343:
4339:
4332:
4329:
4324:
4320:
4313:
4308:
4303:
4300:
4297:
4293:
4289:
4283:
4280:
4277:
4274:
4266:
4258:
4250:
4239:
4232:
4231:
4230:
4227:
4210:
4189:
4169:
4161:
4151:
4134:
4128:
4119:
4103:
4099:
4076:
4072:
4046:
4042:
4035:
4015:
3990:
3986:
3979:
3959:
3935:
3929:
3909:
3886:
3883:
3880:
3877:
3874:
3871:
3863:
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2102:
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2070:
2068:positive real
2067:
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2008:
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1992:
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1955:positive real
1954:
1938:
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1914:
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1881:
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1864:
1857:values, i.e.
1842:
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686:
682:
679:
676:
671:
667:
646:
638:
634:
631:are the only
616:
613:
609:
595:
574:
571:
567:
559:
557:
553:
535:
532:
528:
520:
518:
500:
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229:
224:
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147:
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139:
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132:
128:
124:
120:
110:
108:
104:
100:
96:
92:
90:
84:
73:
70:
62:
52:
48:
42:
39:This article
37:
28:
27:
22:
16770:Concordancer
16739:
16166:Bag-of-words
16100:Apache Spark
16096:LDA in Spark
16010:
15989:
15980:
15965:by removing
15952:
15922:
15909:
15900:
15896:
15890:
15863:
15859:
15855:
15845:
15832:
15825:
15802:
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15782:
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15765:
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15646:
15642:
15594:
15590:
15580:
15535:
15531:
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15500:
15492:
15465:
15461:
15455:
15441:
15396:
15392:
15382:
15347:
15341:
15331:
15320:. Retrieved
15316:the original
15303:
15299:
15293:
15239:
15235:
15231:
15221:
15178:
15174:
15170:
15112:
15108:
14989:
14969:
14942:
14917:
12667:
11048:
10695:denotes the
10655:
10255:
9615:
8373:
7232:
6927:
6419:
5969:
5558:
5397:
5008:
4509:
4228:
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3843:
3700:
3339:
3050:
3043:
3031:
3023:
3007:
2995:
2983:
2639:
2629:
2625:
2595:
2594:in document
2591:
2585:
2509:
2505:
2475:
2474:in document
2471:
2465:
2392:
2385:
2336:
2332:
2263:
2256:
2207:
2203:
2103:
2072:
1990:
1959:
1799:
1647:
1573:The lengths
1572:
1559:
1557:
1291:
1179:
1000:
929:
749:
705:
600:
555:
551:
516:
484:
451:
445:
412:
408:
402:
389:
344:
333:
320:
315:
311:
301:
299:to "bark".)
291:
287:
283:
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247:
243:
239:
235:
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194:
185:
164:
135:
116:
94:
86:
80:
65:
56:
40:
16727:Topic model
16607:Text corpus
16453:Statistical
16320:Text mining
16161:AI-complete
16053:topicmodels
16045:, a Python+
15866:4734–4756.
15653:1261–1280.
15242:1567–1587.
6098:topic. So,
4064:time where
2917:Categorical
2828:Categorical
1564:multinomial
1558:(Note that
1508:Multinomial
1443:Multinomial
342:algorithm.
292:CAT_related
256:DOG_related
176:confounding
131:P. Donnelly
127:M. Stephens
107:topic model
59:August 2017
16881:Categories
16448:Rule-based
16330:Truecasing
16198:Stop words
16110:exampleLDA
15903:: 524–531.
15322:2006-12-19
15306:993–1022.
15149:References
4028:bucket in
3972:bucket in
2978:See also:
1644:Definition
1180:2. Choose
1001:1. Choose
296:stop words
268:Maine coon
191:Musicology
142:David Blei
91:allocation
16757:reviewing
16555:standards
16553:Types and
16080:MapReduce
15983:June 2016
15967:excessive
15757:174802673
15484:225158478
15468:153–164.
15433:260246078
15195:0016-6731
15181:945–959.
15143:Infer.NET
15063:∣
15028:∣
14890:β
14866:−
14846:⋅
14818:∑
14806:β
14782:−
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14737:α
14713:−
14699:⋅
14677:∝
14658:β
14634:−
14614:⋅
14586:∑
14574:β
14550:−
14530:⋅
14505:α
14481:−
14467:⋅
14431:β
14407:−
14387:⋅
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14350:Γ
14334:β
14310:−
14290:⋅
14274:Γ
14262:∏
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14187:Γ
14178:∏
14155:β
14131:−
14111:⋅
14083:∑
14071:β
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13978:−
13964:⋅
13928:β
13904:−
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13831:β
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13787:⋅
13771:Γ
13754:α
13730:−
13716:⋅
13694:Γ
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13633:⋅
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13580:β
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13536:⋅
13520:Γ
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13419:≠
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13215:Γ
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11955:Γ
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11799:Γ
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11722:Γ
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11468:⋅
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11426:∏
11414:≠
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11316:∑
11307:Γ
11283:β
11277:α
11245:−
11199:∝
11186:β
11180:α
11148:−
11139:∣
11029:β
11023:α
10991:−
10982:∣
10819:−
10632:β
10626:α
10594:−
10574:β
10568:α
10536:−
10487:β
10481:α
10449:−
10440:∣
10386:β
10380:α
10335:β
10329:α
10292:β
10286:α
10275:∣
10224:β
10201:⋅
10173:∑
10164:Γ
10150:β
10127:⋅
10113:Γ
10093:∏
10074:β
10067:Γ
10047:∏
10030:β
10009:∑
10000:Γ
9977:∏
9973:×
9956:α
9939:⋅
9905:∑
9896:Γ
9882:α
9865:⋅
9845:Γ
9825:∏
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9799:Γ
9779:∏
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9741:∑
9732:Γ
9709:∏
9699:β
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9625:ϕ
9580:β
9557:⋅
9529:∑
9520:Γ
9506:β
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9469:Γ
9449:∏
9430:β
9423:Γ
9403:∏
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9356:Γ
9333:∏
9310:φ
9297:−
9288:β
9265:⋅
9242:φ
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9205:β
9198:Γ
9178:∏
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9140:∑
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9117:φ
9112:∫
9091:∏
9068:φ
9042:⋅
9019:φ
8998:∏
8989:−
8980:β
8964:φ
8943:∏
8927:β
8920:Γ
8900:∏
8883:β
8862:∑
8853:Γ
8839:φ
8834:∫
8813:∏
8790:φ
8760:φ
8756:∣
8714:∏
8693:∏
8686:β
8674:φ
8656:φ
8651:∫
8630:∏
8612:φ
8582:φ
8578:∣
8536:∏
8515:∏
8508:β
8496:φ
8469:∏
8463:φ
8459:∫
8427:θ
8405:φ
8383:φ
8338:α
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8287:∑
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8264:α
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8227:Γ
8207:∏
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8181:Γ
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8091:θ
8078:−
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8052:⋅
8023:θ
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7986:α
7969:⋅
7949:Γ
7929:∏
7912:α
7895:⋅
7861:∑
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7838:θ
7833:∫
7815:α
7798:⋅
7764:∑
7755:Γ
7741:α
7724:⋅
7704:Γ
7684:∏
7665:α
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7638:∏
7621:α
7600:∑
7591:Γ
7568:θ
7555:−
7546:α
7529:⋅
7500:θ
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7456:Γ
7436:∏
7419:α
7398:∑
7389:Γ
7375:θ
7370:∫
7357:θ
7340:θ
7336:∣
7294:∏
7287:α
7275:θ
7257:θ
7252:∫
7206:θ
7193:−
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7167:⋅
7138:θ
7117:∏
7101:α
7084:⋅
7064:Γ
7044:∏
7027:α
7010:⋅
6976:∑
6967:Γ
6953:θ
6948:∫
6904:θ
6891:−
6882:α
6865:⋅
6836:θ
6815:∏
6799:α
6792:Γ
6772:∏
6755:α
6734:∑
6725:Γ
6711:θ
6706:∫
6693:θ
6673:⋅
6644:θ
6623:∏
6614:−
6605:α
6589:θ
6568:∏
6552:α
6545:Γ
6525:∏
6508:α
6487:∑
6478:Γ
6464:θ
6459:∫
6429:θ
6390:⋅
6361:θ
6340:∏
6324:θ
6320:∣
6278:∏
6184:⋅
6147:⋅
5946:θ
5929:θ
5925:∣
5883:∏
5874:−
5865:α
5849:θ
5828:∏
5812:α
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5785:∏
5768:α
5747:∑
5738:Γ
5724:θ
5719:∫
5706:θ
5689:θ
5685:∣
5643:∏
5636:α
5624:θ
5606:θ
5601:∫
5535:θ
5518:θ
5514:∣
5472:∏
5465:α
5453:θ
5435:θ
5430:∫
5406:θ
5374:θ
5357:θ
5353:∣
5311:∏
5304:α
5292:θ
5274:θ
5269:∫
5248:∏
5240:θ
5223:θ
5219:∣
5177:∏
5170:α
5158:θ
5131:∏
5125:θ
5121:∫
5097:θ
5077:φ
5069:and each
5057:θ
5037:φ
5017:θ
4986:θ
4969:θ
4965:∣
4923:∏
4916:α
4904:θ
4877:∏
4871:θ
4867:∫
4862:φ
4832:φ
4828:∣
4786:∏
4765:∏
4758:β
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4709:∫
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4682:φ
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4650:θ
4622:φ
4618:∫
4612:θ
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4592:α
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4519:φ
4470:φ
4466:∣
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4428:∣
4386:∏
4379:α
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4340:∏
4333:β
4321:φ
4294:∏
4284:β
4278:α
4271:φ
4263:θ
4190:θ
4170:φ
3872:∣
3855:∼
3829:β
3809:α
3683:β
3669:¬
3633:α
3592:∑
3559:β
3545:¬
3530:β
3492:∑
3459:β
3445:¬
3430:β
3427:α
3404:∑
3322:β
3308:¬
3272:α
3242:β
3228:¬
3213:β
3186:β
3172:¬
3157:β
3154:α
3148:∝
2974:Inference
2934:φ
2926:
2913:∼
2894:…
2876:…
2845:θ
2837:
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2805:…
2787:…
2762:α
2755:
2746:Dirichlet
2742:∼
2730:…
2716:θ
2703:β
2696:
2687:Dirichlet
2683:∼
2671:…
2657:φ
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668:φ
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465:θ
368:MapReduce
312:ambiguous
152:in 2003.
146:Andrew Ng
133:in 2000.
89:Dirichlet
16673:Wikidata
16653:FrameNet
16638:BabelNet
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16532:Word2vec
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9544:V
9539:1
9536:=
9533:r
9524:(
9515:)
9510:r
9502:+
9497:i
9492:r
9489:,
9486:)
9480:(
9476:n
9472:(
9464:V
9459:1
9456:=
9453:r
9439:)
9434:r
9426:(
9418:V
9413:1
9410:=
9407:r
9396:)
9390:r
9380:V
9375:1
9372:=
9369:r
9360:(
9348:K
9343:1
9340:=
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9323:=
9314:i
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9300:1
9292:r
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9271:,
9268:)
9262:(
9258:n
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9246:i
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9231:1
9228:=
9225:r
9214:)
9209:r
9201:(
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9185:=
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9165:r
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9150:1
9147:=
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9121:i
9106:K
9101:1
9098:=
9095:i
9081:=
9072:i
9064:d
9056:i
9051:r
9048:,
9045:)
9039:(
9035:n
9029:r
9026:,
9023:i
9013:V
9008:1
9005:=
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8984:r
8974:r
8971:,
8968:i
8958:V
8953:1
8950:=
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8936:)
8931:r
8923:(
8915:V
8910:1
8907:=
8904:r
8893:)
8887:r
8877:V
8872:1
8869:=
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8857:(
8843:i
8828:K
8823:1
8820:=
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8803:=
8794:i
8786:d
8782:)
8775:t
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8769:j
8765:Z
8751:t
8748:,
8745:j
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8737:(
8734:P
8729:N
8724:1
8721:=
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8700:=
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8683:;
8678:i
8670:(
8667:P
8660:i
8645:K
8640:1
8637:=
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8591:j
8587:Z
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8559:(
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8543:=
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8522:=
8519:j
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8505:;
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8476:=
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8342:i
8334:+
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8318:(
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8312:j
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8294:=
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8273:)
8268:i
8260:+
8255:i
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8244:(
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8230:(
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8217:1
8214:=
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8065:+
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8055:)
8049:(
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8027:j
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7990:i
7982:+
7977:i
7972:)
7966:(
7963:,
7960:j
7956:n
7952:(
7944:K
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7936:=
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7922:)
7916:i
7908:+
7903:i
7898:)
7892:(
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7886:j
7882:n
7876:K
7871:1
7868:=
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7856:(
7842:j
7825:)
7819:i
7811:+
7806:i
7801:)
7795:(
7792:,
7789:j
7785:n
7779:K
7774:1
7771:=
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7759:(
7750:)
7745:i
7737:+
7732:i
7727:)
7721:(
7718:,
7715:j
7711:n
7707:(
7699:K
7694:1
7691:=
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7669:i
7661:(
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7648:1
7645:=
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7631:)
7625:i
7615:K
7610:1
7607:=
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7595:(
7581:=
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7564:d
7558:1
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7542:+
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7532:)
7526:(
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7507:,
7504:j
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7489:1
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7467:i
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7443:=
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7429:)
7423:i
7413:K
7408:1
7405:=
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7393:(
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7366:=
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7328:,
7325:j
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7317:(
7314:P
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7301:=
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7279:j
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7215:=
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7158:j
7154:n
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7142:j
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7124:=
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7110:)
7105:i
7097:+
7092:i
7087:)
7081:(
7078:,
7075:j
7071:n
7067:(
7059:K
7054:1
7051:=
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7031:i
7023:+
7018:i
7013:)
7007:(
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7001:j
6997:n
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6986:1
6983:=
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6971:(
6957:j
6913:.
6908:j
6900:d
6894:1
6886:i
6878:+
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6868:)
6862:(
6859:,
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6852:n
6846:i
6843:,
6840:j
6830:K
6825:1
6822:=
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6803:i
6795:(
6787:K
6782:1
6779:=
6776:i
6765:)
6759:i
6749:K
6744:1
6741:=
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6729:(
6715:j
6702:=
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6689:d
6681:i
6676:)
6670:(
6667:,
6664:j
6660:n
6654:i
6651:,
6648:j
6638:K
6633:1
6630:=
6627:i
6617:1
6609:i
6599:i
6596:,
6593:j
6583:K
6578:1
6575:=
6572:i
6561:)
6556:i
6548:(
6540:K
6535:1
6532:=
6529:i
6518:)
6512:i
6502:K
6497:1
6494:=
6491:i
6482:(
6468:j
6433:j
6405:.
6398:i
6393:)
6387:(
6384:,
6381:j
6377:n
6371:i
6368:,
6365:j
6355:K
6350:1
6347:=
6344:i
6336:=
6333:)
6328:j
6315:t
6312:,
6309:j
6305:Z
6301:(
6298:P
6293:N
6288:1
6285:=
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6252:h
6249:t
6245:i
6222:h
6219:t
6215:j
6192:i
6187:)
6181:(
6178:,
6175:j
6171:n
6150:)
6144:(
6122:i
6117:r
6114:,
6111:j
6107:n
6084:h
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6054:h
6051:t
6047:r
6024:h
6021:t
6017:j
5994:i
5989:r
5986:,
5983:j
5979:n
5955:.
5950:j
5942:d
5938:)
5933:j
5920:t
5917:,
5914:j
5910:Z
5906:(
5903:P
5898:N
5893:1
5890:=
5887:t
5877:1
5869:i
5859:i
5856:,
5853:j
5843:K
5838:1
5835:=
5832:i
5821:)
5816:i
5808:(
5800:K
5795:1
5792:=
5789:i
5778:)
5772:i
5762:K
5757:1
5754:=
5751:i
5742:(
5728:j
5715:=
5710:j
5702:d
5698:)
5693:j
5680:t
5677:,
5674:j
5670:Z
5666:(
5663:P
5658:N
5653:1
5650:=
5647:t
5639:)
5633:;
5628:j
5620:(
5617:P
5610:j
5575:h
5572:t
5568:j
5544:.
5539:j
5531:d
5527:)
5522:j
5509:t
5506:,
5503:j
5499:Z
5495:(
5492:P
5487:N
5482:1
5479:=
5476:t
5468:)
5462:;
5457:j
5449:(
5446:P
5439:j
5383:.
5378:j
5370:d
5366:)
5361:j
5348:t
5345:,
5342:j
5338:Z
5334:(
5331:P
5326:N
5321:1
5318:=
5315:t
5307:)
5301:;
5296:j
5288:(
5285:P
5278:j
5263:M
5258:1
5255:=
5252:j
5244:=
5236:d
5232:)
5227:j
5214:t
5211:,
5208:j
5204:Z
5200:(
5197:P
5192:N
5187:1
5184:=
5181:t
5173:)
5167:;
5162:j
5154:(
5151:P
5146:M
5141:1
5138:=
5135:j
4990:.
4982:d
4978:)
4973:j
4960:t
4957:,
4954:j
4950:Z
4946:(
4943:P
4938:N
4933:1
4930:=
4927:t
4919:)
4913:;
4908:j
4900:(
4897:P
4892:M
4887:1
4884:=
4881:j
4858:d
4854:)
4847:t
4844:,
4841:j
4837:Z
4823:t
4820:,
4817:j
4813:W
4809:(
4806:P
4801:N
4796:1
4793:=
4790:t
4780:M
4775:1
4772:=
4769:j
4761:)
4755:;
4750:i
4742:(
4739:P
4734:K
4729:1
4726:=
4723:i
4699:=
4687:d
4678:d
4674:)
4668:,
4662:;
4654:,
4646:,
4642:Z
4638:,
4634:W
4630:(
4627:P
4604:=
4601:)
4595:,
4589:;
4585:W
4581:,
4577:Z
4573:(
4570:P
4495:,
4492:)
4485:t
4482:,
4479:j
4475:Z
4461:t
4458:,
4455:j
4451:W
4447:(
4444:P
4441:)
4436:j
4423:t
4420:,
4417:j
4413:Z
4409:(
4406:P
4401:N
4396:1
4393:=
4390:t
4382:)
4376:;
4371:j
4363:(
4360:P
4355:M
4350:1
4347:=
4344:j
4336:)
4330:;
4325:i
4317:(
4314:P
4309:K
4304:1
4301:=
4298:i
4290:=
4287:)
4281:,
4275:;
4267:,
4259:,
4255:Z
4251:,
4247:W
4243:(
4240:P
4211:N
4138:)
4135:1
4132:(
4129:O
4104:w
4100:K
4077:d
4073:K
4052:)
4047:w
4043:K
4039:(
4036:O
4016:C
3996:)
3991:d
3987:K
3983:(
3980:O
3960:B
3939:)
3936:K
3933:(
3930:O
3910:A
3890:)
3887:C
3884:+
3881:B
3878:+
3875:A
3868:|
3864:s
3861:(
3858:U
3852:s
3789:A
3769:w
3749:C
3729:d
3709:B
3680:V
3677:+
3672:n
3664:k
3660:C
3654:)
3649:d
3644:k
3640:C
3636:+
3630:(
3625:w
3620:k
3616:C
3607:K
3602:1
3599:=
3596:k
3588:=
3585:C
3556:V
3553:+
3548:n
3540:k
3536:C
3525:d
3520:k
3516:C
3507:K
3502:1
3499:=
3496:k
3488:=
3485:B
3456:V
3453:+
3448:n
3440:k
3436:C
3419:K
3414:1
3411:=
3408:k
3400:=
3397:A
3374:c
3354:b
3351:,
3348:a
3319:V
3316:+
3311:n
3303:k
3299:C
3293:)
3288:d
3283:k
3279:C
3275:+
3269:(
3264:w
3259:k
3255:C
3248:+
3239:V
3236:+
3231:n
3223:k
3219:C
3208:d
3203:k
3199:C
3192:+
3183:V
3180:+
3175:n
3167:k
3163:C
3145:)
3142:k
3139:=
3134:n
3131:,
3128:d
3124:Z
3120:(
3117:p
3092:w
3088:K
3065:d
3061:K
2954:)
2947:w
2944:d
2940:z
2929:(
2921:V
2902:d
2898:N
2891:1
2888:=
2885:w
2882:,
2879:M
2873:1
2870:=
2867:d
2863:w
2855:)
2850:d
2840:(
2832:K
2813:d
2809:N
2802:1
2799:=
2796:w
2793:,
2790:M
2784:1
2781:=
2778:d
2774:z
2766:)
2758:(
2750:K
2733:M
2727:1
2724:=
2721:d
2707:)
2699:(
2691:V
2674:K
2668:1
2665:=
2662:k
2630:V
2626:N
2610:W
2596:d
2592:w
2586:V
2566:d
2562:N
2555:1
2552:=
2549:w
2546:,
2543:M
2537:1
2534:=
2531:d
2527:w
2510:K
2506:N
2490:Z
2476:d
2472:w
2466:K
2446:d
2442:N
2435:1
2432:=
2429:w
2426:,
2423:M
2417:1
2414:=
2411:d
2407:z
2393:d
2386:K
2369:M
2363:1
2360:=
2357:d
2337:d
2333:k
2312:K
2306:1
2303:=
2300:k
2297:,
2294:M
2288:1
2285:=
2282:d
2264:k
2257:V
2240:K
2234:1
2231:=
2228:k
2208:k
2204:w
2183:V
2177:1
2174:=
2171:w
2168:,
2165:K
2159:1
2156:=
2153:k
2122:w
2104:V
2073:w
2052:V
2046:1
2043:=
2040:w
2009:k
1991:K
1960:k
1939:K
1933:1
1930:=
1927:k
1897:d
1893:N
1887:M
1882:1
1879:=
1876:d
1868:=
1865:N
1843:d
1839:N
1813:N
1800:d
1779:M
1773:1
1770:=
1767:d
1763:N
1733:M
1704:V
1675:K
1628:z
1608:w
1586:i
1582:N
1543:.
1540:)
1533:j
1530:,
1527:i
1523:z
1514:(
1500:j
1497:,
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1490:w
1465:.
1462:)
1457:i
1449:(
1435:j
1432:,
1429:i
1425:z
1401:}
1396:i
1392:N
1388:,
1382:,
1379:1
1376:{
1370:j
1350:}
1347:M
1344:,
1338:,
1335:1
1332:{
1326:i
1306:j
1303:,
1300:i
1257:}
1254:K
1251:,
1245:,
1242:1
1239:{
1233:k
1213:)
1207:(
1193:k
1165:1
1115:)
1109:(
1105:r
1102:i
1099:D
1078:}
1075:M
1072:,
1066:,
1063:1
1060:{
1054:i
1034:)
1028:(
1014:i
984:i
980:N
959:M
939:D
908:M
900:,
894:,
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735:V
715:V
691:K
683:,
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617:j
614:i
610:w
575:j
572:i
568:w
556:i
552:j
536:j
533:i
529:z
517:k
501:k
485:i
469:i
452:β
446:α
428:i
424:N
413:i
409:N
403:M
93:(
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23:.
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