Word-sense disambiguation

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the greatest word overlap in their dictionary definitions. For example, when disambiguating the words in "pine cone", the definitions of the appropriate senses both include the words evergreen and tree (at least in one dictionary). A similar approach searches for the shortest path between two words: the second word is iteratively searched among the definitions of every semantic variant of the first word, then among the definitions of every semantic variant of each word in the previous definitions and so on. Finally, the first word is disambiguated by selecting the semantic variant which minimizes the distance from the first to the second word.

259:. WSD systems are normally tested by having their results on a task compared against those of a human. However, while it is relatively easy to assign parts of speech to text, training people to tag senses has been proven to be far more difficult. While users can memorize all of the possible parts of speech a word can take, it is often impossible for individuals to memorize all of the senses a word can take. Moreover, humans do not agree on the task at hand – give a list of senses and sentences, and humans will not always agree on which word belongs in which sense. 640:(i.e., short defining gloss and one or more usage example) using a pre-trained word-embedding model. These centroids are later used to select the word sense with the highest similarity of a target word to its immediately adjacent neighbors (i.e., predecessor and successor words). After all words are annotated and disambiguated, they can be used as a training corpus in any standard word-embedding technique. In its improved version, MSSA can make use of word sense embeddings to repeat its disambiguation process iteratively. 632:) objects as nodes and the relationship between nodes as edges. The relations (edges) in AutoExtend can either express the addition or similarity between its nodes. The former captures the intuition behind the offset calculus, while the latter defines the similarity between two nodes. In MSSA, an unsupervised disambiguation system uses the similarity between word senses in a fixed context window to select the most suitable word sense using a pre-trained word-embedding model and 969: 624:) can also assist unsupervised systems in mapping words and their senses as dictionaries. Some techniques that combine lexical databases and word embeddings are presented in AutoExtend and Most Suitable Sense Annotation (MSSA). In AutoExtend, they present a method that decouples an object input representation into its properties, such as words and their word senses. AutoExtend uses a graph structure to map words (e.g. text) and non-word (e.g. 612:) has become one of the most fundamental blocks in several NLP systems. Even though most of traditional word-embedding techniques conflate words with multiple meanings into a single vector representation, they still can be used to improve WSD. A simple approach to employ pre-computed word embeddings to represent word senses is to compute the centroids of sense clusters. In addition to word-embedding techniques, lexical databases (e.g., 101:) level is routinely above 90% (as of 2009), with some methods on particular homographs achieving over 96%. On finer-grained sense distinctions, top accuracies from 59.1% to 69.0% have been reported in evaluation exercises (SemEval-2007, Senseval-2), where the baseline accuracy of the simplest possible algorithm of always choosing the most frequent sense was 51.4% and 57%, respectively. 552:, using any supervised method. This classifier is then used on the untagged portion of the corpus to extract a larger training set, in which only the most confident classifications are included. The process repeats, each new classifier being trained on a successively larger training corpus, until the whole corpus is consumed, or until a given maximum number of iterations is reached. 844:(2007). The objective of the competition is to organize different lectures, preparing and hand-annotating corpus for testing systems, perform a comparative evaluation of WSD systems in several kinds of tasks, including all-words and lexical sample WSD for different languages, and, more recently, new tasks such as 334:– was proposed as a possible solution to the sense discreteness problem. The task consists of providing a substitute for a word in context that preserves the meaning of the original word (potentially, substitutes can be chosen from the full lexicon of the target language, thus overcoming discreteness). 442:

is the seminal dictionary-based method. It is based on the hypothesis that words used together in text are related to each other and that the relation can be observed in the definitions of the words and their senses. Two (or more) words are disambiguated by finding the pair of dictionary senses with

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frequently discover in corpora loose and overlapping word meanings, and standard or conventional meanings extended, modulated, and exploited in a bewildering variety of ways. The art of lexicography is to generalize from the corpus to definitions that evoke and explain the full range of meaning of a

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A task-independent sense inventory is not a coherent concept: each task requires its own division of word meaning into senses relevant to the task. Additionally, completely different algorithms might be required by different applications. In machine translation, the problem takes the form of target

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Both WSD and part-of-speech tagging involve disambiguating or tagging with words. However, algorithms used for one do not tend to work well for the other, mainly because the part of speech of a word is primarily determined by the immediately adjacent one to three words, whereas the sense of a word

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and sense tagging have proven to be very closely related, with each potentially imposing constraints upon the other. The question whether these tasks should be kept together or decoupled is still not unanimously resolved, but recently scientists incline to test these things separately (e.g. in the

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of language examples is also required). WSD task has two variants: "lexical sample" (disambiguating the occurrences of a small sample of target words which were previously selected) and "all words" task (disambiguation of all the words in a running text). "All words" task is generally considered a

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Knowledge is a fundamental component of WSD. Knowledge sources provide data which are essential to associate senses with words. They can vary from corpora of texts, either unlabeled or annotated with word senses, to machine-readable dictionaries, thesauri, glossaries, ontologies, etc. They can be

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to a set of dictionary senses is not desired, cluster-based evaluations (including measures of entropy and purity) can be performed. Alternatively, word sense induction methods can be tested and compared within an application. For instance, it has been shown that word sense induction improves Web

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will provide different divisions of words into senses. Some researchers have suggested choosing a particular dictionary, and using its set of senses to deal with this issue. Generally, however, research results using broad distinctions in senses have been much better than those using narrow ones.

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have been shown to be the most successful approaches, to date, probably because they can cope with the high-dimensionality of the feature space. However, these supervised methods are subject to a new knowledge acquisition bottleneck since they rely on substantial amounts of manually sense-tagged

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Shallow approaches do not try to understand the text, but instead consider the surrounding words. These rules can be automatically derived by the computer, using a training corpus of words tagged with their word senses. This approach, while theoretically not as powerful as deep approaches, gives

321:, and disagreements arise. For example, in Senseval-2, which used fine-grained sense distinctions, human annotators agreed in only 85% of word occurrences. Word meaning is in principle infinitely variable and context-sensitive. It does not divide up easily into distinct or discrete sub-meanings. 357:

and her colleagues, at the Cambridge Language Research Unit in England, in the 1950s. This attempt used as data a punched-card version of Roget's Thesaurus and its numbered "heads", as an indicator of topics and looked for repetitions in text, using a set intersection algorithm. It was not very

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or discrimination. Then, new occurrences of the word can be classified into the closest induced clusters/senses. Performance has been lower than for the other methods described above, but comparisons are difficult since senses induced must be mapped to a known dictionary of word senses. If a

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evaluation task is also focused on WSD across 2 or more languages simultaneously. Unlike the Multilingual WSD tasks, instead of providing manually sense-annotated examples for each sense of a polysemous noun, the sense inventory is built up on the basis of parallel corpora, e.g. Europarl

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was an early example of such an algorithm. It uses the ‘One sense per collocation’ and the ‘One sense per discourse’ properties of human languages for word sense disambiguation. From observation, words tend to exhibit only one sense in most given discourse and in a given collocation.

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more realistic form of evaluation, but the corpus is more expensive to produce because human annotators have to read the definitions for each word in the sequence every time they need to make a tagging judgement, rather than once for a block of instances for the same target word.

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approach starts from a small amount of seed data for each word: either manually tagged training examples or a small number of surefire decision rules (e.g., 'play' in the context of 'bass' almost always indicates the musical instrument). The seeds are used to train an initial

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Comparing and evaluating different WSD systems is extremely difficult, because of the different test sets, sense inventories, and knowledge resources adopted. Before the organization of specific evaluation campaigns most systems were assessed on in-house, often small-scale,

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research of the early days of AI research have been applied with some success. More complex graph-based approaches have been shown to perform almost as well as supervised methods or even outperforming them on specific domains. Recently, it has been reported that simple

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may be determined by words further away. The success rate for part-of-speech tagging algorithms is at present much higher than that for WSD, state-of-the art being around 96% accuracy or better, as compared to less than 75% accuracy in word sense disambiguation with

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is trained for each distinct word on a corpus of manually sense-annotated examples, and completely unsupervised methods that cluster occurrences of words, thereby inducing word senses. Among these, supervised learning approaches have been the most successful

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Classical WSD for other languages uses their respective WordNet as sense inventories and sense annotated corpora tagged in their respective languages. Often researchers will also tapped on the SemCor corpus and aligned bitexts with English as its

295:– that is, 'edge of river'). In information retrieval, a sense inventory is not necessarily required, because it is enough to know that a word is used in the same sense in the query and a retrieved document; what sense that is, is unimportant. 912:

as multilingual sense inventory. It evolved from the Translation WSD evaluation tasks that took place in Senseval-2. A popular approach is to carry out monolingual WSD and then map the source language senses into the corresponding target word

349:. These approaches are generally not considered to be very successful in practice, mainly because such a body of knowledge does not exist in a computer-readable format, outside very limited domains. Additionally due to the long tradition in 852:, etc. The systems submitted for evaluation to these competitions usually integrate different techniques and often combine supervised and knowledge-based methods (especially for avoiding bad performance in lack of training examples). 486:

The use of selectional preferences (or selectional restrictions) is also useful, for example, knowing that one typically cooks food, one can disambiguate the word bass in "I am cooking basses" (i.e., it's not a musical instrument).

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implement simple and robust IR techniques that can successfully mine the Web for information to use in WSD. The historic lack of training data has provoked the appearance of some new algorithms and techniques, as described in

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In recent years , the WSD evaluation task choices had grown and the criterion for evaluating WSD has changed drastically depending on the variant of the WSD evaluation task. Below enumerates the variety of WSD tasks:

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in Hindi have hindered the performance of supervised models of WSD, while the unsupervised models suffer due to extensive morphology. A possible solution to this problem is the design of a WSD model by means of

166:, semi-supervised and unsupervised corpus-based systems, combinations of different methods, and the return of knowledge-based systems via graph-based methods. Still, supervised systems continue to perform best. 817:. In order to test one's algorithm, developers should spend their time to annotate all word occurrences. And comparing methods even on the same corpus is not eligible if there is different sense inventories. 2184:. Proc. of seventh International Workshop on Semantic Evaluation (SemEval), in the Second Joint Conference on Lexical and Computational Semantics (*SEM 2013), Atlanta, USA, June 14–15th, 2013, pp. 222–231. 2711:. Proc. of the 44th Annual Meeting of the Association for Computational Linguistics joint with the 21st International Conference on Computational Linguistics. Sydney, Australia: COLING-ACL. Archived from 353:, of trying such approaches in terms of coded knowledge and in some cases, it can be hard to distinguish between knowledge involved in linguistic or world knowledge. The first attempt was that by 719:

depend crucially on the existence of manually annotated examples for every word sense, a requisite that can so far be met only for a handful of words for testing purposes, as it is done in the

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from Knowledge to WordNet has been shown to boost simple knowledge-based methods, enabling them to rival the best supervised systems and even outperform them in a domain-specific setting.

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WSD was first formulated as a distinct computational task during the early days of machine translation in the 1940s, making it one of the oldest problems in computational linguistics.

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word selection. The "senses" are words in the target language, which often correspond to significant meaning distinctions in the source language ("bank" could translate to the French

734:, to acquire lexical information automatically. WSD has been traditionally understood as an intermediate language engineering technology which could improve applications such as 156:(OALD), became available: hand-coding was replaced with knowledge automatically extracted from these resources, but disambiguation was still knowledge-based or dictionary-based. 953:

UKB: Graph Base WSD, a collection of programs for performing graph-based Word Sense Disambiguation and lexical similarity/relatedness using a pre-existing Lexical Knowledge Base

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search result clustering by increasing the quality of result clusters and the degree diversification of result lists. It is hoped that unsupervised learning will overcome the

2557:. Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics. Rochester, New York: HLT-NAACL. 159:

In the 1990s, the statistical revolution advanced computational linguistics, and WSD became a paradigm problem on which to apply supervised machine learning techniques.

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is the greatest challenge for WSD researchers. The underlying assumption is that similar senses occur in similar contexts, and thus senses can be induced from text by

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Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing

382:: These make use of a secondary source of knowledge such as a small annotated corpus as seed data in a bootstrapping process, or a word-aligned bilingual corpus. 3276: 149:' preference semantics. However, since WSD systems were at the time largely rule-based and hand-coded they were prone to a knowledge acquisition bottleneck. 2204:. In EACL-2006 Workshop on Making Sense of Sense: Bringing Psycholinguistics and Computational Linguistics Together, pages 33–40, Trento, Italy, April 2006. 2144:. In EACL-2006 Workshop on Making Sense of Sense: Bringing Psycholinguistics and Computational Linguistics Together, pages 33–40, Trento, Italy, April 2006. 3436: 559:

information that supplements the tagged corpora. These techniques have the potential to help in the adaptation of supervised models to different domains.

2921:. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. EMNLP-CoNLL. 864:

As technology evolves, the Word Sense Disambiguation (WSD) tasks grows in different flavors towards various research directions and for more languages:

2932:. Proc. of the 3rd International Workshop on the Evaluation of Systems for the Semantic Analysis of Text (Senseval-3). Barcelona, Spain. Archived from 562:

Also, an ambiguous word in one language is often translated into different words in a second language depending on the sense of the word. Word-aligned

430:. Graph-based approaches have also gained much attention from the research community, and currently achieve performance close to the state of the art. 394:: These eschew (almost) completely external information and work directly from raw unannotated corpora. These methods are also known under the name of 2887:. Proc. of Semeval-2007 Workshop (SEMEVAL), in the 45th Annual Meeting of the Association for Computational Linguistics. Prague, Czech Republic: ACL. 326:

word, making it seem like words are well-behaved semantically. However, it is not at all clear if these same meaning distinctions are applicable in

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sets (e.g. the concept of car is encoded as { car, auto, automobile, machine, motorcar }). Other resources used for disambiguation purposes include

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successful, but had strong relationships to later work, especially Yarowsky's machine learning optimisation of a thesaurus method in the 1990s.

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Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

636:. For each context window, MSSA calculates the centroid of each word sense definition by averaging the word vectors of its words in WordNet's 3414: 950:

WordNet::SenseRelate, a project that includes free, open source systems for word sense disambiguation and lexical sample sense disambiguation

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are deemed unnecessary). Probably every machine learning algorithm going has been applied to WSD, including associated techniques such as

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In the 1970s, WSD was a subtask of semantic interpretation systems developed within the field of artificial intelligence, starting with

2049:. Proceedings of the 40th Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2002. 2029:. Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics. Association for Computational Linguistics, 2004. 1111: 908:

evaluation tasks focused on WSD across 2 or more languages simultaneously, using their respective WordNets as its sense inventories or

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Mikolov, Tomas; Chen, Kai; Corrado, Greg; Dean, Jeffrey (2013-01-16). "Efficient Estimation of Word Representations in Vector Space".

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Diamantini, C.; Mircoli, A.; Potena, D.; Storti, E. (2015-06-01). "Semantic disambiguation in a social information discovery system".

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distinctions, so this again is why research on coarse-grained distinctions has been put to test in recent WSD evaluation exercises.

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Ruas, Terry; Grosky, William; Aizawa, Akiko (December 2019). "Multi-sense embeddings through a word sense disambiguation process".

327: 2676:. Proc. of the North American Chapter of the Association for Computational Linguistics. Rochester, New York: NAACL. Archived from 3994: 142:(1960) argued that WSD could not be solved by "electronic computer" because of the need in general to model all world knowledge. 2858: 2157: 944:

BabelNet API, a Java API for knowledge-based multilingual Word Sense Disambiguation in 6 different languages using the BabelNet

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Edmonds, Philip; Kilgarriff, Adam (2002). "Introduction to the special issue on evaluating word sense disambiguation systems".

2732:. Proc. of the 2010 Conference on Empirical Methods in Natural Language Processing. MIT Stata Center, Massachusetts, US: EMNLP. 2697:. Proceedings of the 11th Conference on European chapter of the Association for Computational Linguistics. Trento, Italy: EACL. 475:, perform state-of-the-art WSD in the presence of a sufficiently rich lexical knowledge base. Also, automatically transferring 81:

Many techniques have been researched, including dictionary-based methods that use the knowledge encoded in lexical resources,

2084: 1992: 1435: 1338: 1259: 2224:. Proceedings of the 4th International Workshop on Semantic Evaluations, pp. 7–12, June 23–24, 2007, Prague, Czech Republic. 1998: 97:

Accuracy of current algorithms is difficult to state without a host of caveats. In English, accuracy at the coarse-grained (

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Given that natural language requires reflection of neurological reality, as shaped by the abilities provided by the brain's

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Buitelaar, P.; Magnini, B.; Strapparava, C.; Vossen, P. (2006). "Domain-specific WSD". In Agirre, E.; Edmonds, P. (eds.).

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methods are based on the assumption that the context can provide enough evidence on its own to disambiguate words (hence,

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has paved way for several Supervised methods which have been proven to produce a higher accuracy in disambiguating nouns.

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The 2000s saw supervised techniques reach a plateau in accuracy, and so attention has shifted to coarser-grained senses,

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Rothe, Sascha; Schütze, Hinrich (2015). "AutoExtend: Extending Word Embeddings to Embeddings for Synsets and Lexemes".

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Pennington, Jeffrey; Socher, Richard; Manning, Christopher (2014). "Glove: Global Vectors for Word Representation".

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Automatic sense disambiguation using machine readable dictionaries: How to tell a pine cone from an ice cream cone

2529:. Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing. Barcelona, Spain: EMNLP. 927:

data, consisting of polysemous words and the sentence that they occurred in, then WSD is performed on a different

3938: 3591: 2543:. Proceedings of ACL Workshop on Word Sense Disambiguation: Recent Successes and Future Directions. Philadelphia. 2288: 662: 20: 2364: 1721: 1681: 3586: 3331: 1612:

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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In order to define common evaluation datasets and procedures, public evaluation campaigns have been organized.

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rely on knowledge about word senses, which is only sparsely formulated in dictionaries and lexical databases.

3855: 3576: 2724: 2338: 2794:), in the 45th Annual Meeting of the Association for Computational Linguistics. Prague, Czech Republic: ACL. 3548: 1708:

Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics

459: 2852:. Proceedings of the 2nd Workshop on Scalable Natural Language Understanding Systems in HLT/NAACL. Boston. 2486: 3893: 3878: 3850: 3715: 3710: 3285: 2800: 2632: 982: 763: 468: 369: 346: 330:, as the decisions of lexicographers are usually driven by other considerations. In 2009, a task – named 82: 71: 3193:"Distinguishing systems and distinguishing senses: New evaluation methods for word sense disambiguation" 2866:. Proc. of the 48th Annual Meeting of the Association for Computational Linguistics. ACL. Archived from 2738: 2313: 941:

Babelfy, a unified state-of-the-art system for multilingual Word Sense Disambiguation and Entity Linking

4045: 3630: 3601: 3379: 3156: 1704:"ShotgunWSD: An unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing" 1656:

Bhingardive, Sudha; Singh, Dhirendra; V, Rudramurthy; Redkar, Hanumant; Bhattacharyya, Pushpak (2015).

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first introduced the problem in a computational context in his 1949 memorandum on translation. Later,

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corpora have been used to infer cross-lingual sense distinctions, a kind of semi-supervised system.

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content words around each word to be disambiguated in the corpus, and statistically analyzing those

3999: 3923: 3655: 3611: 3496: 3394: 3075: 2214: 876: 672: 549: 532: 379: 244:. These figures are typical for English, and may be very different from those for other languages. 86: 27: 2406:

Agirre, E.; Stevenson, M. (2007). "Knowledge sources for WSD". In Agirre, E.; Edmonds, P. (eds.).

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Constraint-based Grammar Formalisms: Parsing and Type Inference for Natural and Computer Languages

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as it sense inventory and the primary classification input is normally based on the SemCor corpus.

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Word-sense disambiguation using statistical models of Roget's categories trained on large corpora

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Lucia Specia, Maria das Gracas Volpe Nunes, Gabriela Castelo Branco Ribeiro, and Mark Stevenson.

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Lucia Specia, Maria das Gracas Volpe Nunes, Gabriela Castelo Branco Ribeiro, and Mark Stevenson.

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The knowledge acquisition bottleneck is perhaps the major impediment to solving the WSD problem.

515: 253: 3374: 828:) is an international word sense disambiguation competition, held every three years since 1998: 3760: 3453: 3431: 3421: 3389: 3364: 2801:"Structural Semantic Interconnections: a Knowledge-Based Approach to Word Sense Disambiguation" 2598:. Proceedings of the 2nd Conference on Language Resources and Evaluation. Athens, Greece: LREC. 2453: 845: 617: 423: 411: 228: 52: 48: 1524:

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

1423: 70:, computer science has had a long-term challenge in developing the ability in computers to do 3620: 2580:. Proc. of SIGDOC-86: 5th International Conference on Systems Documentation. Toronto, Canada. 2572: 735: 712: 602: 580: 519: 472: 391: 1107: 3973: 3649: 3625: 3478: 2913: 2585:

Litkowski, K. C. (2005). "Computational lexicons and dictionaries". In Brown, K. R. (ed.).

2447:. Proceedings of the 20th National Conference on Artificial Intelligence. Pittsburgh: AAAI. 2019: 1761: 997: 920: 849: 592: 575: 463: 372:- and knowledge-based methods: These rely primarily on dictionaries, thesauri, and lexical 331: 208: 2677: 2549: 8: 3953: 3883: 3840: 3796: 3568: 3558: 3553: 3441: 3046: 2933: 2494: 1007: 872: 790: 716: 495: 451: 427: 385: 241: 179:

One problem with word sense disambiguation is deciding what the senses are, as different

139: 2739:"An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation" 2392:"Knowledge-based WSD on Specific Domains: Performing better than Generic Supervised WSD" 1765: 4050: 4040: 3963: 3835: 3700: 3463: 3446: 3304: 3212: 3179: 3134: 3098: 3033: 2831: 2769: 2655: 2506: 2473: 2391: 2058:

Manish Sinha, Mahesh Kumar, Prabhakar Pande, Laxmi Kashyap, and Pushpak Bhattacharyya.

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Proc. of ANLP-97 Workshop on Tagging Text with Lexical Semantics: Why, What, and How?

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Other semi-supervised techniques use large quantities of untagged corpora to provide

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language independent NLU combining Patom Theory and RRG (Role and Reference Grammar)

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Because of the lack of training data, many word sense disambiguation algorithms use

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Bojanowski, Piotr; Grave, Edouard; Joulin, Armand; Mikolov, Tomas (December 2017).

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Yarowsky, David (2001). "Word sense disambiguation". In Dale; et al. (eds.).

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surrounding words. Two shallow approaches used to train and then disambiguate are

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competitions parts of speech are provided as input for the text to disambiguate).

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Meaningful Clustering of Senses Helps Boost Word Sense Disambiguation Performance

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Semeval-2007 task 02: evaluating word sense induction and discrimination systems

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Representing words considering their context through fixed-size dense vectors (

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An alternative to the use of the definitions is to consider general word-sense

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There are two main approaches to WSD – deep approaches and shallow approaches.

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level (e.g., pen as writing instrument or enclosure), but go down one level to

308: 267: 3254: 3208: 3094: 3029: 2712: 2689: 2651: 2535: 2194: 2134: 1914: 699: 307:" is slippery and controversial. Most people can agree in distinctions at the 219:, a multilingual encyclopedic dictionary, has been used for multilingual WSD. 4019: 3928: 3740: 3720: 3501: 1825: 1592: 1330: 1323:

2015 International Conference on Collaboration Technologies and Systems (CTS)

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pyWSD, python implementations of Word Sense Disambiguation (WSD) technologies

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evaluation tasks use WordNet as the sense inventory and are largely based on

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superior results in practice, due to the computer's limited world knowledge.

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of each pair of word senses based on a given lexical knowledge base such as

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for computer performance. Human performance, however, is much better on

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Iacobacci, Ignacio; Pilehvar, Mohammad Taher; Navigli, Roberto (2016).

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as a reference sense inventory for English. WordNet is a computational

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Unsupervised domain relevance estimation for word sense disambiguation

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Unsupervised sense disambiguation using bilingual probabilistic models

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One of the most promising trends in WSD research is using the largest

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Computational Linguistics Special Issue on Word Sense Disambiguation

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Knowledge-rich Word Sense Disambiguation rivaling supervised systems

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An unsupervised method for word sense tagging using parallel corpora

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corpora for training, which are laborious and expensive to create.

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Unsupervised word sense disambiguation rivaling supervised methods

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Butnaru, Andrei; Ionescu, Radu Tudor; Hristea, Florentina (2017).

1658:"Unsupervised Most Frequent Sense Detection using Word Embeddings" 1507: 1266: 1108:

Entity Linking meets Word Sense Disambiguation: a Unified Approach

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is a combined task evaluation where the sense inventory is first

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SemEval-2007 Task 17: English lexical sample, SRL and all words

1938: 1800:"AutoExtend: Combining Word Embeddings with Semantic Resources" 1608:"Embeddings for Word Sense Disambiguation: An Evaluation Study" 2808:

IEEE Transactions on Pattern Analysis and Machine Intelligence

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IEEE Transactions on Pattern Analysis and Machine Intelligence

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Transactions of the Association for Computational Linguistics

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Inducing Word Senses to Improve Web Search Result Clustering

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SemEval-2013 Task 12: Multilingual Word Sense Disambiguation

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SemEval-2010 task 3: cross-lingual word sense disambiguation

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bottleneck because they are not dependent on manual effort.

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SemEval-2007 Task 07: Coarse-Grained English All-Words Task

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McCarthy, D.; Koeling, R.; Weeds, J.; Carroll, J. (2007).

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classification with the manually sense annotated corpora:

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Deep approaches presume access to a comprehensive body of

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to specify the senses which are to be disambiguated and a

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Wiley Interdisciplinary Reviews: Computational Statistics

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Almost all these approaches work by defining a window of

3112:"Introduction to the special issue on the Web as corpus" 2878:

Pradhan, S.; Loper, E.; Dligach, D.; Palmer, M. (2007).

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Different sense granularities for different applications

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sfn error: no target: CITEREFKilgarrifGrefenstette2003 (

2018:

Bhattacharya, Indrajit, Lise Getoor, and Yoshua Bengio.

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Other approaches may vary differently in their methods:

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Most research in the field of WSD is performed by using

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By the 1980s large-scale lexical resources, such as the

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Snow, R.; Prakash, S.; Jurafsky, D.; Ng, A. Y. (2007).

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Using Knowledge for Automatic Word Sense Disambiguation

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Scaling up word sense disambiguation via parallel texts

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Oxford Advanced Learner's Dictionary of Current English

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For information on Knowledge disambiguation pages, see

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Foundations of Statistical Natural Language Processing

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Word Sense Disambiguation: Algorithms and Applications

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Word Sense Disambiguation: Algorithms and Applications

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Word Sense Disambiguation: Algorithms and Applications

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As human performance serves as the standard, it is an

2911: 2605:"Unsupervised acquisition of predominant word senses" 2078: 1701: 1302: 1278: 1217: 19:"Disambiguation" redirects here. For other uses, see 16:

Identification of which sense of a word is being used

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Electric Words: dictionaries, computers and meanings

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Agirre, E.; Lopez de Lacalle, A.; Soroa, A. (2009).

964: 535:, which allows both labeled and unlabeled data. The 2489:(Tech. note). Brighton, UK: University of Brighton. 1235: 1175: 1088: 174: 3146:Manning, Christopher D.; Schütze, Hinrich (1999). 2956: 2843:Palmer, M.; Babko-Malaya, O.; Dang, H. T. (2004). 2781:Navigli, R.; Litkowski, K.; Hargraves, O. (2007). 2691:Determining word sense dominance using a thesaurus 2551:An information retrieval approach to sense ranking 2520:Gliozzo, A.; Magnini, B.; Strapparava, C. (2004). 1798:Rothe, Sascha; Schütze, Hinrich (September 2017). 1296: 3047:"Word sense disambiguation: The state of the art" 2950:Machine Translation of Languages: Fourteen Essays 2722: 1557:"Enriching Word Vectors with Subword Information" 1473: 1020: 4017: 3487: 1841: 1428:The Oxford Handbook of Computational Linguistics 1134:"Part-of-speech tagging: Part-of-speech tagging" 706: 3284: 2856: 2630: 2593: 2533: 2451: 2405: 2121: 2109: 1956: 1485: 1397: 1272: 380:Semi-supervised or minimally supervised methods 365:There are four conventional approaches to WSD: 278:Sense inventory and algorithms' task-dependency 2798: 1361: 3270: 2997:Agirre, Eneko; Edmonds, Philip, eds. (2007). 2736: 2687: 1932: 1385: 750: 745:Automatic acquisition of sense-tagged corpora 121:data to be disambiguated (in some methods, a 109:Disambiguation requires two strict inputs: a 3228:. New York: Marcel Dekker. pp. 629–654. 2925: 2596:Integrating subject field codes into WordNet 2589:(2nd ed.). Oxford: Elsevier Publishers. 2547: 1944: 1797: 1741: 1258:sfn error: no target: CITEREFKilgarrif1997 ( 1193: 917:Word Sense Induction and Disambiguation task 290: 284: 3065:Jurafsky, Daniel; Martin, James H. (2000). 2957:Wilks, Y.; Slator, B.; Guthrie, L. (1996). 1974: 3277: 3263: 2537:Sense discrimination with parallel corpora 2511:: CS1 maint: location missing publisher ( 2497:(1997). "Analysis of a handwriting task". 2424: 1038: 526: 3251:by Rada Mihalcea and Ted Pedersen (2005). 3110:Kilgarriff, A.; Grefenstette, G. (2003). 2587:Encyclopaedia of Language and Linguistics 2584: 2561: 2097: 1873: 1855: 1815: 1755: 1715: 1629: 1619: 1582: 1572: 1531: 1506: 1374:Agirre, Lopez de Lacalle & Soroa 2009 1284: 1253: 1120:Association for Computational Linguistics 803:: raw corpora and sense-annotated corpora 337: 298: 222: 3191:Resnik, Philip; Yarowsky, David (2000). 2976: 2965: 2666: 2534:Ide, N.; Erjavec, T.; Tufis, D. (2002). 2493: 2173:R. Navigli, D. A. Jurgens, D. Vannella. 1409: 1181: 1131: 1094: 1082: 3226:Handbook of Natural Language Processing 3018:Journal of Natural Language Engineering 2891: 2701: 2633:"The English Lexical Substitution Task" 2484: 1980: 1897:Gliozzo, Magnini & Strapparava 2004 1461: 1241: 1205: 1056:Navigli, Litkowski & Hargraves 2007 655:Identification of dominant word senses; 569: 434:Dictionary- and knowledge-based methods 247: 4018: 3150:. Cambridge, Massachusetts: MIT Press. 2961:. Cambridge, Massachusetts: MIT Press. 2948:. In Locke, W.N.; Booth, A.D. (eds.). 2940: 2564:Building Large Knowledge-Based Systems 2442: 1968: 1421: 1026: 859: 3258: 3157:"Word Sense Disambiguation: A Survey" 2894:"Automatic word sense discrimination" 2857:Ponzetto, S. P.; Navigli, R. (2010). 2358: 1837: 1835: 1793: 1791: 1496: 1494: 490: 188:Most researchers continue to work on 3736:Simple Knowledge Organization System 2723:Navigli, R.; Crisafulli, G. (2010). 2570: 1308: 1230:Palmer, Babko-Malaya & Dang 2004 376:, without using any corpus evidence. 39:is the process of identifying which 2195:Multilingual versus monolingual WSD 2135:Multilingual versus monolingual WSD 1132:Martinez, Angel R. (January 2012). 643: 426:have shown superior performance in 13: 3248:Word Sense Disambiguation Tutorial 3045:Ide, Nancy; Véronis, Jean (1998). 2989: 2790:. Proc. of Semeval-2007 Workshop ( 2631:McCarthy, D.; Navigli, R. (2009). 2594:Magnini, B.; Cavaglià, G. (2000). 2452:Di Marco, A.; Navigli, R. (2013). 1832: 1788: 1491: 1106:A. Moro; A. Raganato; R. Navigli. 678: 591:of context, a task referred to as 14: 4062: 3751:Thesaurus (information retrieval) 3234: 2799:Navigli, R.; Velardi, P. (2005). 2640:Language Resources and Evaluation 2487:"Designing a task for SENSEVAL-2" 2337:. Moin.delph-in.net. 2018-02-05. 2153:Els Lefever and Veronique Hoste. 2079:Kilgarrif & Grefenstette 2003 658:WSD using Cross-Lingual Evidence. 3076:"I don't believe in word senses" 3069:. New Jersey, US: Prentice Hall. 2737:Navigli, R.; Lapata, M. (2010). 2688:Mohammad, S.; Hirst, G. (2006). 1844:Expert Systems with Applications 1424:"13.5.3 Two claims about senses" 1297:Wilks, Slator & Guthrie 1996 1003:Sentence boundary disambiguation 967: 175:Differences between dictionaries 2926:Snyder, B.; Palmer, M. (2004). 2562:Lenat, D.; Guha, R. V. (1989). 2548:Lapata, M.; Keller, F. (2007). 2443:Chan, Y. S.; Ng, H. T. (2005). 2367:from the original on 2018-06-11 2352: 2341:from the original on 2018-03-09 2327: 2316:from the original on 2018-03-12 2302: 2291:from the original on 2018-03-21 2287:. Senserelate.sourceforge.net. 2277: 2266:from the original on 2018-03-22 2252: 2241:from the original on 2014-08-08 2227: 2207: 2187: 2167: 2147: 2127: 2060:Hindi word sense disambiguation 2052: 2038:Diab, Mona, and Philip Resnik. 2032: 2012: 2001:from the original on 2023-07-15 1735: 1724:from the original on 2023-01-21 1695: 1684:from the original on 2023-01-21 1649: 1638:from the original on 2019-10-28 1599: 1548: 1515: 1444:from the original on 2022-02-22 1415: 1314: 1164:from the original on 2023-07-15 289:– that is, 'financial bank' or 169: 21:Disambiguation (disambiguation) 3332:Natural language understanding 3067:Speech and Language Processing 2429:. Reading, MA: Addison-Wesley. 2382: 2335:"Lexical Knowledge Base (LKB)" 2213:Eneko Agirre and Aitor Soroa. 1125: 1100: 510:, parameter optimization, and 63:, it is usually subconscious. 1: 3856:Optical character recognition 2915:Learning to Merge Word Senses 1957:Ide, Erjavec & Tufis 2002 1474:Navigli & Crisafulli 2010 1122:(TACL). 2. pp. 231–244. 2014. 1013: 882:Classic English WSD uses the 807: 764:Machine-readable dictionaries 707:Local impediments and summary 652:Domain-driven disambiguation; 303:Finally, the very notion of " 3549:Multi-document summarization 3197:Natural Language Engineering 1987:. Massachusetts: MIT Press. 587:word occurrences using some 7: 4031:Natural language processing 3879:Latent Dirichlet allocation 3851:Natural language generation 3716:Machine-readable dictionary 3711:Linguistic Linked Open Data 3286:Natural language processing 2952:. Cambridge, MA: MIT Press. 2667:Mihalcea, R. (April 2007). 2122:Magnini & Cavaglià 2000 2110:Agirre & Stevenson 2007 1981:Shieber, Stuart M. (1992). 1486:Di Marco & Navigli 2013 1398:Ponzetto & Navigli 2010 1273:McCarthy & Navigli 2009 983:Controlled natural language 960: 935: 840:(2004), and its successor, 469:graph connectivity measures 104: 83:supervised machine learning 72:natural language processing 10: 4067: 3631:Explicit semantic analysis 3380:Deep linguistic processing 3131:10.1162/089120103322711569 2929:The English all-words task 2752:(4). IEEE Press: 678–692. 2624:10.1162/coli.2007.33.4.553 1866:10.1016/j.eswa.2019.06.026 1362:Navigli & Velardi 2005 751:External knowledge sources 573: 328:computational applications 129: 25: 18: 4036:Computational linguistics 4026:Word-sense disambiguation 3982: 3937: 3892: 3864: 3824: 3769: 3691: 3679: 3610: 3567: 3539: 3474:Word-sense disambiguation 3350: 3327:Computational linguistics 3292: 3209:10.1017/S1351324999002211 3155:Navigli, Roberto (2009). 3119:Computational Linguistics 3054:Computational Linguistics 3030:10.1017/S1351324902002966 2901:Computational Linguistics 2652:10.1007/s10579-009-9084-1 2612:Computational Linguistics 2464:(3). MIT Press: 709–754. 2458:Computational Linguistics 1933:Mohammad & Hirst 2006 1804:Computational Linguistics 1386:Navigli & Lapata 2010 789:Other resources (such as 673:constraint-based grammars 396:word sense discrimination 351:computational linguistics 203:that encodes concepts as 4000:Natural Language Toolkit 3924:Pronunciation assessment 3826:Automatic identification 3656:Latent semantic analysis 3612:Distributional semantics 3497:Compound-term processing 3395:Named-entity recognition 2646:(2). Springer: 139–159. 2427:Language and information 1945:Lapata & Keller 2007 1331:10.1109/CTS.2015.7210442 1194:Snyder & Palmer 2004 533:semi-supervised learning 28:Knowledge:Disambiguation 3904:Automated essay scoring 3874:Document classification 3541:Automatic summarization 3176:10.1145/1459352.1459355 3095:10.1023/A:1000583911091 3074:Kilgarriff, A. (1997). 2941:Weaver, Warren (1949). 2425:Bar-Hillel, Y. (1964). 1422:Mitkov, Ruslan (2004). 993:Judicial interpretation 869:Classic monolingual WSD 756:classified as follows: 527:Semi-supervised methods 516:Support Vector Machines 462:methods reminiscent of 424:support vector machines 412:Naïve Bayes classifiers 3761:Universal Dependencies 3454:Terminology extraction 3437:Semantic decomposition 3432:Semantic role labeling 3422:Part-of-speech tagging 3390:Information extraction 3375:Coreference resolution 3365:Collocation extraction 2820:10.1109/TPAMI.2005.149 2410:. New York: Springer. 2285:"WordNet::SenseRelate" 1118:. Transactions of the 846:semantic role labeling 797:, domain labels, etc.) 698:. The creation of the 418:. In recent research, 338:Approaches and methods 299:Discreteness of senses 291: 285: 229:part-of-speech tagging 223:Part-of-speech tagging 3522:Sentence segmentation 3164:ACM Computing Surveys 2977:Yarowsky, D. (1995). 2966:Yarowsky, D. (1992). 2758:10.1109/TPAMI.2009.36 2438:. New York: Springer. 2310:"UKB: Graph Base WSD" 2124:, pp. 1413–1418. 1971:, pp. 1037–1042. 1909:Buitelaar et al. 2006 1744:Volume 1: Long Papers 1400:, pp. 1522–1531. 1376:, pp. 1501–1506. 1364:, pp. 1063–1074. 1285:Lenat & Guha 1989 1220:, pp. 1005–1014. 785:Collocation resources 736:information retrieval 730:ever accessible, the 603:knowledge acquisition 589:measure of similarity 581:Unsupervised learning 520:memory-based learning 37:-sense disambiguation 3974:Voice user interface 3685:datasets and corpora 3626:Document-term matrix 3479:Word-sense induction 2892:Schütze, H. (1998). 2702:Navigli, R. (2006). 2495:Fellbaum, Christiane 2485:Edmonds, P. (2000). 2470:10.1162/COLI_a_00148 1921:McCarthy et al. 2007 1817:10.1162/coli_a_00294 1621:10.18653/v1/P16-1085 1584:10.1162/tacl_a_00051 1430:. OUP. p. 257. 1325:. pp. 326–333. 998:Semantic unification 850:lexical substitution 791:word frequency lists 713:Unsupervised methods 593:word sense induction 576:Word sense induction 570:Unsupervised methods 464:spreading activation 420:kernel-based methods 392:Unsupervised methods 332:lexical substitution 248:Inter-judge variance 51:or other segment of 3954:Interactive fiction 3884:Pachinko allocation 3841:Speech segmentation 3797:Google Ngram Viewer 3569:Machine translation 3559:Text simplification 3554:Sentence extraction 3442:Semantic similarity 2112:, pp. 217–251. 2100:, pp. 753–761. 2081:, pp. 333–347. 1947:, pp. 348–355. 1935:, pp. 121–128. 1923:, pp. 553–590. 1911:, pp. 275–298. 1899:, pp. 380–387. 1774:10.3115/v1/p15-1173 1766:2015arXiv150701127R 1670:10.3115/v1/N15-1132 1533:10.3115/v1/d14-1162 1412:, pp. 189–196. 1388:, pp. 678–692. 1275:, pp. 139–159. 1208:, pp. 105–112. 1085:, pp. 454–460. 1071:Pradhan et al. 2007 1041:, pp. 174–179. 1008:Syntactic ambiguity 860:Task design choices 452:semantic similarity 450:and to compute the 428:supervised learning 252:Another problem is 242:supervised learning 85:methods in which a 57:language processing 3964:Question answering 3836:Speech recognition 3701:Corpus linguistics 3681:Language resources 3464:Textual entailment 3447:Sentiment analysis 2312:. Ixa2.si.ehu.es. 2220:2013-02-28 at the 2200:2012-04-10 at the 2180:2014-08-08 at the 2160:2010-06-16 at the 2140:2012-04-10 at the 2065:2016-03-04 at the 2045:2016-03-04 at the 2025:2016-01-09 at the 1969:Chan & Ng 2005 1464:, pp. 97–123. 1256:, pp. 91–113. 1114:2014-08-08 at the 975:Linguistics portal 740:web search engines 717:Supervised methods 537:Yarowsky algorithm 491:Supervised methods 481:semantic relations 386:Supervised methods 355:Margaret Masterman 227:In any real test, 4046:Lexical semantics 4013: 4012: 3969:Virtual assistant 3894:Computer-assisted 3820: 3819: 3577:Computer-assisted 3535: 3534: 3527:Word segmentation 3489:Text segmentation 3427:Semantic analysis 3415:Syntactic parsing 3400:Ontology learning 2571:Lesk, M. (1986). 2566:. Addison-Wesley. 2501:. Washington D.C. 1994:978-0-262-19324-5 1959:, pp. 54–60. 1437:978-0-19-927634-9 1340:978-1-4673-7647-1 1311:, pp. 24–26. 1232:, pp. 49–56. 1196:, pp. 41–43. 1073:, pp. 87–92. 1058:, pp. 30–35. 899:Cross-lingual WSD 884:Princeton WordNet 691:lexical resources 512:ensemble learning 508:feature selection 215:. More recently, 209:Roget's Thesaurus 164:domain adaptation 4058: 3990:Formal semantics 3939:Natural language 3846:Speech synthesis 3828:and data capture 3731:Semantic network 3706:Lexical resource 3689: 3688: 3507:Lexical analysis 3485: 3484: 3410:Semantic parsing 3279: 3272: 3265: 3256: 3255: 3229: 3220: 3187: 3161: 3151: 3142: 3116: 3106: 3080: 3070: 3061: 3051: 3041: 3012: 2984: 2973: 2962: 2953: 2947: 2937: 2922: 2920: 2908: 2898: 2888: 2886: 2874: 2872: 2865: 2853: 2851: 2839: 2814:(7): 1075–1086. 2805: 2795: 2789: 2777: 2743: 2733: 2731: 2719: 2717: 2710: 2698: 2696: 2684: 2682: 2675: 2663: 2637: 2627: 2609: 2599: 2590: 2581: 2579: 2567: 2558: 2556: 2544: 2542: 2530: 2528: 2516: 2510: 2502: 2490: 2481: 2448: 2439: 2430: 2421: 2402: 2396: 2376: 2375: 2373: 2372: 2356: 2350: 2349: 2347: 2346: 2331: 2325: 2324: 2322: 2321: 2306: 2300: 2299: 2297: 2296: 2281: 2275: 2274: 2272: 2271: 2262:. 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1036: 1030: 1024: 977: 972: 971: 946:semantic network 929:testing data set 906:Multilingual WSD 696:parallel corpora 661:WSD solution in 644:Other approaches 294: 288: 76:machine learning 4066: 4065: 4061: 4060: 4059: 4057: 4056: 4055: 4016: 4015: 4014: 4009: 3978: 3958:Syntax guessing 3940: 3933: 3919:Predictive text 3914:Grammar checker 3895: 3888: 3860: 3827: 3816: 3782:Bank of English 3765: 3693: 3684: 3675: 3606: 3563: 3531: 3483: 3385:Distant reading 3360:Argument mining 3346: 3342:Text processing 3288: 3283: 3237: 3232: 3223: 3190: 3159: 3154: 3145: 3114: 3109: 3078: 3073: 3064: 3049: 3044: 3015: 3009: 2996: 2992: 2990:Further reading 2987: 2945: 2918: 2896: 2884: 2870: 2863: 2849: 2803: 2787: 2741: 2729: 2715: 2708: 2694: 2680: 2673: 2635: 2607: 2577: 2554: 2540: 2526: 2504: 2503: 2418: 2394: 2385: 2380: 2379: 2370: 2368: 2357: 2353: 2344: 2342: 2333: 2332: 2328: 2319: 2317: 2308: 2307: 2303: 2294: 2292: 2283: 2282: 2278: 2269: 2267: 2258: 2257: 2253: 2244: 2242: 2233: 2232: 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1033: 1025: 1021: 1016: 973: 966: 963: 938: 892:source language 877:semi-supervised 862: 810: 753: 709: 681: 679:Other languages 646: 610:word embeddings 578: 572: 529: 493: 479:in the form of 436: 374:knowledge bases 347:world knowledge 340: 301: 280: 250: 225: 177: 172: 132: 123:training corpus 107: 68:neural networks 31: 24: 17: 12: 11: 5: 4064: 4054: 4053: 4048: 4043: 4038: 4033: 4028: 4011: 4010: 4008: 4007: 4002: 3997: 3992: 3986: 3984: 3980: 3979: 3977: 3976: 3971: 3966: 3961: 3951: 3945: 3943: 3941:user interface 3935: 3934: 3932: 3931: 3926: 3921: 3916: 3911: 3906: 3900: 3898: 3890: 3889: 3887: 3886: 3881: 3876: 3870: 3868: 3862: 3861: 3859: 3858: 3853: 3848: 3843: 3838: 3832: 3830: 3822: 3821: 3818: 3817: 3815: 3814: 3809: 3804: 3799: 3794: 3789: 3784: 3779: 3773: 3771: 3767: 3766: 3764: 3763: 3758: 3753: 3748: 3743: 3738: 3733: 3728: 3723: 3718: 3713: 3708: 3703: 3697: 3695: 3686: 3677: 3676: 3674: 3673: 3668: 3666:Word embedding 3663: 3658: 3653: 3646:Language model 3643: 3638: 3633: 3628: 3623: 3617: 3615: 3608: 3607: 3605: 3604: 3599: 3597:Transfer-based 3594: 3589: 3584: 3579: 3573: 3571: 3565: 3564: 3562: 3561: 3556: 3551: 3545: 3543: 3537: 3536: 3533: 3532: 3530: 3529: 3524: 3519: 3514: 3509: 3504: 3499: 3493: 3491: 3482: 3481: 3476: 3471: 3466: 3461: 3456: 3450: 3449: 3444: 3439: 3434: 3429: 3424: 3419: 3418: 3417: 3412: 3402: 3397: 3392: 3387: 3382: 3377: 3372: 3370:Concept mining 3367: 3362: 3356: 3354: 3348: 3347: 3345: 3344: 3339: 3334: 3329: 3324: 3323: 3322: 3317: 3307: 3302: 3296: 3294: 3290: 3289: 3282: 3281: 3274: 3267: 3259: 3253: 3252: 3244: 3236: 3235:External links 3233: 3231: 3230: 3221: 3203:(2): 113–133. 3188: 3152: 3143: 3125:(3): 333–347. 3107: 3071: 3062: 3042: 3024:(4): 279–291. 3013: 3008:978-1402068706 3007: 2993: 2991: 2988: 2986: 2985: 2974: 2963: 2954: 2938: 2936:on 2011-06-29. 2923: 2909: 2889: 2875: 2873:on 2011-09-30. 2854: 2840: 2796: 2778: 2734: 2720: 2718:on 2011-06-29. 2699: 2685: 2683:on 2008-07-24. 2664: 2628: 2618:(4): 553–590. 2600: 2591: 2582: 2568: 2559: 2545: 2531: 2517: 2491: 2482: 2449: 2440: 2431: 2422: 2417:978-1402068706 2416: 2403: 2399:Proc. of IJCAI 2386: 2384: 2381: 2378: 2377: 2363:. Github.com. 2351: 2326: 2301: 2276: 2260:"BabelNet API" 2251: 2226: 2206: 2186: 2166: 2146: 2126: 2114: 2102: 2098:Litkowski 2005 2090: 2071: 2051: 2031: 2011: 1993: 1973: 1961: 1949: 1937: 1925: 1913: 1901: 1889: 1875:2027.42/145475 1831: 1810:(3): 593–617. 1787: 1734: 1694: 1648: 1598: 1547: 1514: 1490: 1478: 1466: 1454: 1436: 1414: 1402: 1390: 1378: 1366: 1354: 1339: 1313: 1301: 1289: 1277: 1265: 1254:Kilgarrif 1997 1246: 1234: 1222: 1210: 1198: 1186: 1174: 1144:(1): 107–113. 1124: 1099: 1087: 1075: 1060: 1043: 1031: 1018: 1017: 1015: 1012: 1011: 1010: 1005: 1000: 995: 990: 988:Entity linking 985: 979: 978: 962: 959: 958: 957: 954: 951: 948: 942: 937: 934: 933: 932: 914: 903: 896: 895: 894: 887: 861: 858: 809: 806: 805: 804: 798: 787: 780:Unstructured: 778: 777: 772: 767: 752: 749: 732:World Wide Web 708: 705: 704: 703: 680: 677: 676: 675: 669:Type inference 666: 659: 656: 653: 645: 642: 574:Main article: 571: 568: 528: 525: 492: 489: 440:Lesk algorithm 435: 432: 416:decision trees 400: 399: 389: 383: 377: 339: 336: 323:Lexicographers 309:coarse-grained 300: 297: 279: 276: 268:coarse-grained 249: 246: 224: 221: 176: 173: 171: 168: 131: 128: 106: 103: 47:is meant in a 15: 9: 6: 4: 3: 2: 4063: 4052: 4049: 4047: 4044: 4042: 4039: 4037: 4034: 4032: 4029: 4027: 4024: 4023: 4021: 4006: 4003: 4001: 3998: 3996: 3995:Hallucination 3993: 3991: 3988: 3987: 3985: 3981: 3975: 3972: 3970: 3967: 3965: 3962: 3959: 3955: 3952: 3950: 3947: 3946: 3944: 3942: 3936: 3930: 3929:Spell checker 3927: 3925: 3922: 3920: 3917: 3915: 3912: 3910: 3907: 3905: 3902: 3901: 3899: 3897: 3891: 3885: 3882: 3880: 3877: 3875: 3872: 3871: 3869: 3867: 3863: 3857: 3854: 3852: 3849: 3847: 3844: 3842: 3839: 3837: 3834: 3833: 3831: 3829: 3823: 3813: 3810: 3808: 3805: 3803: 3800: 3798: 3795: 3793: 3790: 3788: 3785: 3783: 3780: 3778: 3775: 3774: 3772: 3768: 3762: 3759: 3757: 3754: 3752: 3749: 3747: 3744: 3742: 3741:Speech corpus 3739: 3737: 3734: 3732: 3729: 3727: 3724: 3722: 3721:Parallel text 3719: 3717: 3714: 3712: 3709: 3707: 3704: 3702: 3699: 3698: 3696: 3690: 3687: 3682: 3678: 3672: 3669: 3667: 3664: 3662: 3659: 3657: 3654: 3651: 3647: 3644: 3642: 3639: 3637: 3634: 3632: 3629: 3627: 3624: 3622: 3619: 3618: 3616: 3613: 3609: 3603: 3600: 3598: 3595: 3593: 3590: 3588: 3585: 3583: 3582:Example-based 3580: 3578: 3575: 3574: 3572: 3570: 3566: 3560: 3557: 3555: 3552: 3550: 3547: 3546: 3544: 3542: 3538: 3528: 3525: 3523: 3520: 3518: 3515: 3513: 3512:Text chunking 3510: 3508: 3505: 3503: 3502:Lemmatisation 3500: 3498: 3495: 3494: 3492: 3490: 3486: 3480: 3477: 3475: 3472: 3470: 3467: 3465: 3462: 3460: 3457: 3455: 3452: 3451: 3448: 3445: 3443: 3440: 3438: 3435: 3433: 3430: 3428: 3425: 3423: 3420: 3416: 3413: 3411: 3408: 3407: 3406: 3403: 3401: 3398: 3396: 3393: 3391: 3388: 3386: 3383: 3381: 3378: 3376: 3373: 3371: 3368: 3366: 3363: 3361: 3358: 3357: 3355: 3353: 3352:Text analysis 3349: 3343: 3340: 3338: 3335: 3333: 3330: 3328: 3325: 3321: 3318: 3316: 3313: 3312: 3311: 3308: 3306: 3303: 3301: 3298: 3297: 3295: 3293:General terms 3291: 3287: 3280: 3275: 3273: 3268: 3266: 3261: 3260: 3257: 3250: 3249: 3245: 3242: 3239: 3238: 3227: 3222: 3218: 3214: 3210: 3206: 3202: 3198: 3194: 3189: 3185: 3181: 3177: 3173: 3169: 3165: 3158: 3153: 3149: 3144: 3140: 3136: 3132: 3128: 3124: 3120: 3113: 3108: 3104: 3100: 3096: 3092: 3089:(2): 91–113. 3088: 3084: 3083:Comput. Human 3077: 3072: 3068: 3063: 3059: 3055: 3048: 3043: 3039: 3035: 3031: 3027: 3023: 3019: 3014: 3010: 3004: 3000: 2995: 2994: 2982: 2981: 2975: 2971: 2970: 2964: 2960: 2955: 2951: 2944: 2943:"Translation" 2939: 2935: 2931: 2930: 2924: 2917: 2916: 2910: 2906: 2902: 2895: 2890: 2883: 2882: 2876: 2869: 2862: 2861: 2855: 2848: 2847: 2841: 2837: 2833: 2829: 2825: 2821: 2817: 2813: 2809: 2802: 2797: 2793: 2786: 2785: 2779: 2775: 2771: 2767: 2763: 2759: 2755: 2751: 2747: 2740: 2735: 2728: 2727: 2721: 2714: 2707: 2706: 2700: 2693: 2692: 2686: 2679: 2672: 2671: 2665: 2661: 2657: 2653: 2649: 2645: 2641: 2634: 2629: 2625: 2621: 2617: 2613: 2606: 2601: 2597: 2592: 2588: 2583: 2576: 2575: 2569: 2565: 2560: 2553: 2552: 2546: 2539: 2538: 2532: 2525: 2524: 2518: 2514: 2508: 2500: 2496: 2492: 2488: 2483: 2479: 2475: 2471: 2467: 2463: 2459: 2455: 2450: 2446: 2441: 2437: 2432: 2428: 2423: 2419: 2413: 2409: 2404: 2400: 2393: 2388: 2387: 2366: 2362: 2355: 2340: 2336: 2330: 2315: 2311: 2305: 2290: 2286: 2280: 2265: 2261: 2255: 2240: 2236: 2230: 2223: 2219: 2216: 2210: 2203: 2199: 2196: 2190: 2183: 2179: 2176: 2170: 2163: 2159: 2156: 2150: 2143: 2139: 2136: 2130: 2123: 2118: 2111: 2106: 2099: 2094: 2086: 2080: 2075: 2068: 2064: 2061: 2055: 2048: 2044: 2041: 2035: 2028: 2024: 2021: 2015: 2000: 1996: 1990: 1986: 1985: 1977: 1970: 1965: 1958: 1953: 1946: 1941: 1934: 1929: 1922: 1917: 1910: 1905: 1898: 1893: 1885: 1881: 1876: 1871: 1867: 1863: 1858: 1853: 1849: 1845: 1838: 1836: 1827: 1823: 1818: 1813: 1809: 1805: 1801: 1794: 1792: 1783: 1779: 1775: 1771: 1767: 1763: 1758: 1753: 1749: 1745: 1738: 1723: 1718: 1713: 1709: 1705: 1698: 1683: 1679: 1675: 1671: 1667: 1663: 1659: 1652: 1637: 1632: 1627: 1622: 1617: 1613: 1609: 1602: 1594: 1590: 1585: 1580: 1575: 1570: 1566: 1562: 1558: 1551: 1543: 1539: 1534: 1529: 1525: 1518: 1509: 1504: 1497: 1495: 1487: 1482: 1475: 1470: 1463: 1458: 1443: 1439: 1433: 1429: 1425: 1418: 1411: 1410:Yarowsky 1995 1406: 1399: 1394: 1387: 1382: 1375: 1370: 1363: 1358: 1350: 1346: 1342: 1336: 1332: 1328: 1324: 1317: 1310: 1305: 1298: 1293: 1286: 1281: 1274: 1269: 1261: 1255: 1250: 1243: 1238: 1231: 1226: 1219: 1214: 1207: 1202: 1195: 1190: 1183: 1182:Fellbaum 1997 1178: 1163: 1159: 1155: 1151: 1147: 1143: 1139: 1135: 1128: 1121: 1117: 1113: 1109: 1103: 1096: 1095:Mihalcea 2007 1091: 1084: 1083:Yarowsky 1992 1079: 1072: 1067: 1065: 1057: 1052: 1050: 1048: 1040: 1035: 1028: 1023: 1019: 1009: 1006: 1004: 1001: 999: 996: 994: 991: 989: 986: 984: 981: 980: 976: 970: 965: 955: 952: 949: 947: 943: 940: 939: 930: 926: 923:from a fixed 922: 918: 915: 913:translations. 911: 907: 904: 900: 897: 893: 888: 885: 881: 880: 878: 874: 870: 867: 866: 865: 857: 853: 851: 848:, gloss WSD, 847: 843: 839: 835: 831: 827: 824:(now renamed 823: 818: 816: 802: 799: 796: 792: 788: 786: 783: 782: 781: 776: 773: 771: 768: 765: 762: 761: 760: 757: 748: 746: 741: 737: 733: 729: 724: 722: 718: 714: 701: 700:Hindi WordNet 697: 692: 688: 687: 683: 682: 674: 670: 667: 664: 660: 657: 654: 651: 650: 649: 641: 639: 635: 631: 627: 623: 619: 615: 611: 606: 604: 599: 594: 590: 586: 582: 577: 567: 565: 560: 558: 557:co-occurrence 553: 551: 546: 545:bootstrapping 541: 538: 534: 524: 521: 517: 513: 509: 505: 501: 497: 488: 484: 482: 478: 474: 470: 465: 461: 457: 453: 449: 444: 441: 431: 429: 425: 421: 417: 413: 409: 405: 397: 393: 390: 387: 384: 381: 378: 375: 371: 368: 367: 366: 363: 359: 356: 352: 348: 343: 335: 333: 329: 324: 320: 317: 313: 310: 306: 296: 293: 287: 275: 273: 269: 265: 260: 258: 255: 245: 243: 237: 235: 230: 220: 218: 214: 210: 206: 202: 198: 193: 191: 186: 182: 167: 165: 160: 157: 155: 150: 148: 143: 141: 137: 136:Warren Weaver 127: 124: 120: 116: 112: 102: 100: 95: 93: 88: 84: 79: 77: 73: 69: 64: 62: 58: 54: 50: 46: 42: 38: 36: 29: 22: 3909:Concordancer 3473: 3305:Bag-of-words 3247: 3225: 3200: 3196: 3167: 3163: 3147: 3122: 3118: 3086: 3082: 3066: 3057: 3053: 3021: 3017: 3001:. Springer. 2998: 2979: 2968: 2958: 2949: 2934:the original 2928: 2914: 2907:(1): 97–123. 2904: 2900: 2880: 2868:the original 2859: 2845: 2811: 2807: 2783: 2749: 2745: 2725: 2713:the original 2704: 2690: 2678:the original 2669: 2643: 2639: 2615: 2611: 2595: 2586: 2573: 2563: 2550: 2536: 2522: 2498: 2461: 2457: 2444: 2435: 2426: 2407: 2398: 2369:. Retrieved 2354: 2343:. Retrieved 2329: 2318:. Retrieved 2304: 2293:. Retrieved 2279: 2268:. Retrieved 2254: 2243:. Retrieved 2229: 2209: 2189: 2169: 2149: 2129: 2117: 2105: 2093: 2074: 2054: 2034: 2014: 2003:. Retrieved 1983: 1976: 1964: 1952: 1940: 1928: 1916: 1904: 1892: 1847: 1843: 1807: 1803: 1747: 1743: 1737: 1726:. Retrieved 1707: 1697: 1686:. Retrieved 1661: 1651: 1640:. Retrieved 1631:11573/936571 1611: 1601: 1564: 1560: 1550: 1523: 1517: 1481: 1469: 1462:Schütze 1998 1457: 1446:. Retrieved 1427: 1417: 1405: 1393: 1381: 1369: 1357: 1322: 1316: 1304: 1292: 1280: 1268: 1249: 1242:Edmonds 2000 1237: 1225: 1213: 1206:Navigli 2006 1201: 1189: 1177: 1166:. Retrieved 1141: 1137: 1127: 1102: 1090: 1078: 1034: 1022: 925:training set 863: 854: 819: 811: 779: 759:Structured: 758: 754: 725: 710: 684: 647: 607: 579: 561: 554: 542: 530: 500:common sense 494: 485: 445: 437: 407: 403: 401: 364: 360: 344: 341: 316:fine-grained 302: 281: 272:fine-grained 261: 251: 238: 226: 194: 190:fine-grained 181:dictionaries 178: 170:Difficulties 161: 158: 151: 144: 133: 108: 96: 80: 65: 33: 32: 3866:Topic model 3746:Text corpus 3592:Statistical 3459:Text mining 3300:AI-complete 3170:(2): 1–69. 2383:Works cited 2359:alvations. 2237:. Babelfy. 1850:: 288–303. 1710:: 916–926. 1567:: 135–146. 1027:Weaver 1949 723:exercises. 663:John Ball's 460:Graph-based 448:relatedness 264:upper bound 254:inter-judge 185:thesauruses 55:. In human 4020:Categories 3587:Rule-based 3469:Truecasing 3337:Stop words 3060:(1): 1–40. 2371:2018-03-22 2345:2018-03-22 2320:2018-03-22 2295:2018-03-22 2270:2018-03-22 2245:2018-03-22 2005:2018-12-23 1857:2101.08700 1757:1507.01127 1728:2023-01-21 1717:1707.08084 1688:2023-01-21 1642:2019-10-28 1574:1607.04606 1448:2022-02-22 1168:2021-04-01 1014:References 873:supervised 838:Senseval-3 834:Senseval-2 830:Senseval-1 808:Evaluation 770:Ontologies 689:: Lack of 618:ConceptNet 585:clustering 550:classifier 496:Supervised 471:, such as 370:Dictionary 305:word sense 140:Bar-Hillel 111:dictionary 92:algorithms 87:classifier 4051:Ambiguity 4041:Semantics 3896:reviewing 3694:standards 3692:Types and 2507:cite book 2235:"Babelfy" 1826:0891-2017 1593:2307-387X 1508:1301.3781 1309:Lesk 1986 815:data sets 795:stoplists 564:bilingual 504:reasoning 477:knowledge 312:homograph 232:Senseval/ 213:Knowledge 99:homograph 94:to date. 61:cognition 3812:Wikidata 3792:FrameNet 3777:BabelNet 3756:Treebank 3726:PropBank 3671:Word2vec 3636:fastText 3517:Stemming 3217:19915022 3038:17866880 2836:12898695 2828:16013755 2766:20224123 2660:16888516 2365:Archived 2339:Archived 2314:Archived 2289:Archived 2264:Archived 2239:Archived 2218:Archived 2198:Archived 2178:Archived 2158:Archived 2138:Archived 2063:Archived 2043:Archived 2023:Archived 1999:Archived 1884:52225306 1782:15687295 1722:Archived 1682:Archived 1678:10778029 1636:Archived 1442:Archived 1349:13260353 1162:Archived 1158:62672734 1112:Archived 961:See also 936:Software 910:BabelNet 836:(2001), 832:(1998), 822:Senseval 775:Thesauri 721:Senseval 622:BabelNet 422:such as 319:polysemy 257:variance 217:BabelNet 119:language 105:Variants 49:sentence 3983:Related 3949:Chatbot 3807:WordNet 3787:DBpedia 3661:Seq2seq 3405:Parsing 3320:Trigram 3139:2649448 3103:3265361 2792:SemEval 2774:1454904 2478:1775181 2361:"pyWSD" 1762:Bibcode 1542:1957433 921:induced 902:corpus. 842:SemEval 826:SemEval 801:Corpora 638:glosses 634:WordNet 630:WordNet 626:synsets 614:WordNet 598:mapping 456:WordNet 234:SemEval 205:synonym 201:lexicon 197:WordNet 130:History 53:context 3956:(c.f. 3614:models 3602:Neural 3315:Bigram 3310:n-gram 3243:(1998) 3215: 3184:461624 3182: 3137: 3101: 3036: 3005: 2834: 2826: 2772: 2764: 2658: 2476: 2414: 1991: 1882: 1824: 1780: 1676: 1591: 1540: 1434: 1347: 1337: 1156: 766:(MRDs) 728:corpus 473:degree 286:banque 115:corpus 4005:spaCy 3650:large 3641:GloVe 3213:S2CID 3180:S2CID 3160:(PDF) 3135:S2CID 3115:(PDF) 3099:S2CID 3079:(PDF) 3050:(PDF) 3034:S2CID 2946:(PDF) 2919:(PDF) 2897:(PDF) 2885:(PDF) 2871:(PDF) 2864:(PDF) 2850:(PDF) 2832:S2CID 2804:(PDF) 2788:(PDF) 2770:S2CID 2742:(PDF) 2730:(PDF) 2716:(PDF) 2709:(PDF) 2695:(PDF) 2681:(PDF) 2674:(PDF) 2656:S2CID 2636:(PDF) 2608:(PDF) 2578:(PDF) 2555:(PDF) 2541:(PDF) 2527:(PDF) 2474:S2CID 2395:(PDF) 1880:S2CID 1852:arXiv 1778:S2CID 1752:arXiv 1712:arXiv 1674:S2CID 1569:arXiv 1538:S2CID 1503:arXiv 1345:S2CID 1154:S2CID 686:Hindi 270:than 192:WSD. 147:Wilks 43:of a 41:sense 3770:Data 3621:BERT 3003:ISBN 2824:PMID 2762:PMID 2513:link 2412:ISBN 2085:help 1989:ISBN 1822:ISSN 1589:ISSN 1432:ISBN 1335:ISBN 1260:help 543:The 518:and 502:and 438:The 414:and 292:rive 211:and 183:and 74:and 59:and 45:word 35:Word 3802:UBY 3205:doi 3172:doi 3127:doi 3091:doi 3026:doi 2816:doi 2754:doi 2648:doi 2620:doi 2466:doi 1870:hdl 1862:doi 1848:136 1812:doi 1770:doi 1666:doi 1626:hdl 1616:doi 1579:doi 1528:doi 1327:doi 1146:doi 671:in 628:in 117:of 4022:: 3211:. 3199:. 3195:. 3178:. 3168:41 3166:. 3162:. 3133:. 3123:29 3121:. 3117:. 3097:. 3087:31 3085:. 3081:. 3058:24 3056:. 3052:. 3032:. 3020:. 2905:24 2903:. 2899:. 2830:. 2822:. 2812:27 2810:. 2806:. 2768:. 2760:. 2750:32 2748:. 2744:. 2654:. 2644:43 2642:. 2638:. 2616:33 2614:. 2610:. 2509:}} 2505:{{ 2472:. 2462:39 2460:. 2456:. 2397:. 1997:. 1878:. 1868:. 1860:. 1846:. 1834:^ 1820:. 1808:43 1806:. 1802:. 1790:^ 1776:. 1768:. 1760:. 1720:. 1706:. 1680:. 1672:. 1660:. 1634:. 1624:. 1610:. 1587:. 1577:. 1563:. 1559:. 1536:. 1493:^ 1440:. 1426:. 1343:. 1333:. 1160:. 1152:. 1140:. 1136:. 1110:. 1063:^ 1046:^ 793:, 747:. 620:, 616:, 514:. 458:. 78:. 3960:) 3683:, 3652:) 3648:( 3278:e 3271:t 3264:v 3219:. 3207:: 3201:5 3186:. 3174:: 3141:. 3129:: 3105:. 3093:: 3040:. 3028:: 3022:8 3011:. 2838:. 2818:: 2776:. 2756:: 2662:. 2650:: 2626:. 2622:: 2515:) 2480:. 2468:: 2420:. 2401:. 2374:. 2348:. 2323:. 2298:. 2273:. 2248:. 2087:) 2008:. 1886:. 1872:: 1864:: 1854:: 1828:. 1814:: 1784:. 1772:: 1764:: 1754:: 1731:. 1714:: 1691:. 1668:: 1645:. 1628:: 1618:: 1595:. 1581:: 1571:: 1565:5 1544:. 1530:: 1511:. 1505:: 1488:. 1476:. 1451:. 1351:. 1329:: 1299:. 1287:. 1262:) 1244:. 1184:. 1171:. 1148:: 1142:4 1097:. 1029:. 931:. 875:/ 408:n 404:n 398:. 30:. 23:.

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