717:. The lexeme's type combined with its value is what properly constitutes a token, which can be given to a parser. Some tokens such as parentheses do not really have values, and so the evaluator function for these can return nothing: Only the type is needed. Similarly, sometimes evaluators can suppress a lexeme entirely, concealing it from the parser, which is useful for whitespace and comments. The evaluators for identifiers are usually simple (literally representing the identifier), but may include some
948:
sometimes used, but modern lexer generators produce faster lexers than most hand-coded ones. The lex/flex family of generators uses a table-driven approach which is much less efficient than the directly coded approach. With the latter approach the generator produces an engine that directly jumps to follow-up states via goto statements. Tools like
466:
is the conversion of a raw text into (semantically or syntactically) meaningful lexical tokens, belonging to categories defined by a "lexer" program, such as identifiers, operators, grouping symbols, and data types. The resulting tokens are then passed on to some other form of processing. The process
1075:
in languages that use braces for blocks and means that the phrase grammar does not depend on whether braces or indenting are used. This requires that the lexer hold state, namely a stack of indent levels, and thus can detect changes in indenting when this changes, and thus the lexical grammar is not
529:, and explicit definition by a dictionary. Special characters, including punctuation characters, are commonly used by lexers to identify tokens because of their natural use in written and programming languages. A lexical analyzer generally does nothing with combinations of tokens, a task left for a
1092:
There are exceptions, however. Simple examples include semicolon insertion in Go, which requires looking back one token; concatenation of consecutive string literals in Python, which requires holding one token in a buffer before emitting it (to see if the next token is another string literal); and
1088:
Generally lexical grammars are context-free, or almost so, and thus require no looking back or ahead, or backtracking, which allows a simple, clean, and efficient implementation. This also allows simple one-way communication from lexer to parser, without needing any information flowing back to the
1001:
Many languages use the semicolon as a statement terminator. Most often this is mandatory, but in some languages the semicolon is optional in many contexts. This is mainly done at the lexer level, where the lexer outputs a semicolon into the token stream, despite one not being present in the input
961:
Lexical analysis mainly segments the input stream of characters into tokens, simply grouping the characters into pieces and categorizing them. However, the lexing may be significantly more complex; most simply, lexers may omit tokens or insert added tokens. Omitting tokens, notably whitespace and
1104:
names and variable names are lexically identical but constitute different token classes. Thus in the hack, the lexer calls the semantic analyzer (say, symbol table) and checks if the sequence requires a typedef name. In this case, information must flow back not from the parser only, but from the
947:
Lexer performance is a concern, and optimizing is worthwhile, more so in stable languages where the lexer is run very often (such as C or HTML). lex/flex-generated lexers are reasonably fast, but improvements of two to three times are possible using more tuned generators. Hand-written lexers are
943:
These tools yield very fast development, which is very important in early development, both to get a working lexer and because a language specification may change often. Further, they often provide advanced features, such as pre- and post-conditions which are hard to program by hand. However, an
725:
may pass the string on (deferring evaluation to the semantic analysis phase), or may perform evaluation themselves, which can be involved for different bases or floating point numbers. For a simple quoted string literal, the evaluator needs to remove only the quotes, but the evaluator for an
682:
characters. In many cases, the first non-whitespace character can be used to deduce the kind of token that follows and subsequent input characters are then processed one at a time until reaching a character that is not in the set of characters acceptable for that token (this is termed the
1010:. In these cases, semicolons are part of the formal phrase grammar of the language, but may not be found in input text, as they can be inserted by the lexer. Optional semicolons or other terminators or separators are also sometimes handled at the parser level, notably in the case of
1093:
the off-side rule in Python, which requires maintaining a count of indent level (indeed, a stack of each indent level). These examples all only require lexical context, and while they complicate a lexer somewhat, they are invisible to the parser and later phases.
44:
belonging to categories defined by a "lexer" program. In case of a natural language, those categories include nouns, verbs, adjectives, punctuations etc. In case of a programming language, the categories include identifiers, operators, grouping symbols and
1036:
Semicolon insertion (in languages with semicolon-terminated statements) and line continuation (in languages with newline-terminated statements) can be seen as complementary: Semicolon insertion adds a token even though newlines generally do
796:-based language, an IDENTIFIER token might be any English alphabetic character or an underscore, followed by any number of instances of ASCII alphanumeric characters and/or underscores. This could be represented compactly by the string
952:
have proven to produce engines that are between two and three times faster than flex produced engines. It is in general difficult to hand-write analyzers that perform better than engines generated by these latter tools.
1067:, where increasing the indenting results in the lexer emitting an INDENT token and decreasing the indenting results in the lexer emitting one or more DEDENT tokens. These tokens correspond to the opening brace
851:
In languages that use inter-word spaces (such as most that use the Latin alphabet, and most programming languages), this approach is fairly straightforward. However, even here there are many edge cases such as
819:. It takes a full parser to recognize such patterns in their full generality. A parser can push parentheses on a stack and then try to pop them off and see if the stack is empty at the end (see example in the
776:
are more practical for a larger number of potential tokens. These tools generally accept regular expressions that describe the tokens allowed in the input stream. Each regular expression is associated with a
598:; they define the set of possible character sequences (lexemes) of a token. A lexer recognizes strings, and for each kind of string found, the lexical program takes an action, most simply producing a token.
670:(FSM). It has encoded within it information on the possible sequences of characters that can be contained within any of the tokens it handles (individual instances of these character sequences are termed
709:, however, is only a string of characters known to be of a certain kind (e.g., a string literal, a sequence of letters). In order to construct a token, the lexical analyzer needs a second stage, the
868:(which for some purposes may count as single tokens). A classic example is "New York-based", which a naive tokenizer may break at the space even though the better break is (arguably) at the hyphen.
781:
in the lexical grammar of the programming language that evaluates the lexemes matching the regular expression. These tools may generate source code that can be compiled and executed or construct a
833:
Typically, lexical tokenization occurs at the word level. However, it is sometimes difficult to define what is meant by a "word". Often, a tokenizer relies on simple heuristics, for example:
697:
over previously read characters. For example, in C, one 'L' character is not enough to distinguish between an identifier that begins with 'L' and a wide-character string literal.
85:
in processing. Analysis generally occurs in one pass. Lexers and parsers are most often used for compilers, but can be used for other computer language tools, such as
894:
Some ways to address the more difficult problems include developing more complex heuristics, querying a table of common special cases, or fitting the tokens to a
506:
When a token class represents more than one possible lexeme, the lexer often saves enough information to reproduce the original lexeme, so that it can be used in
540:, which is a list of number representations. For example, "Identifier" can be represented with 0, "Assignment operator" with 1, "Addition operator" with 2, etc.
1029:, though the rules are somewhat complex and much-criticized; to avoid bugs, some recommend always using semicolons, while others use initial semicolons, termed
1431:
637:
also needs to output the comments and some debugging tools may provide messages to the programmer showing the original source code. In the 1960s, notably for
1474:
1634:
989:
to the prior line. This is generally done in the lexer: The backslash and newline are discarded, rather than the newline being tokenized. Examples include
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comments, is very common when these are not needed by the compiler. Less commonly, added tokens may be inserted. This is done mainly to group tokens into
977:
is a feature of some languages where a newline is normally a statement terminator. Most often, ending a line with a backslash (immediately followed by a
128:
processing to make input easier and simplify the parser, and may be written partly or fully by hand, either to support more features or for performance.
1174:
492:
speaker would do. The raw input, the 43 characters, must be explicitly split into the 9 tokens with a given space delimiter (i.e., matching the string
821:
1612:
533:. For example, a typical lexical analyzer recognizes parentheses as tokens but does nothing to ensure that each "(" is matched with a ")".
2233:
769:
Lexers may be written by hand. This is practical if the list of tokens is small, but lexers generated by automated tooling as part of a
629:
with indenting, initial whitespace is significant, as it determines block structure, and is generally handled at the lexer level; see
2023:
1467:
944:
automatically generated lexer may lack flexibility, and thus may require some manual modification, or an all-manually written lexer.
555:. The lexical analyzer (generated automatically by a tool like lex or hand-crafted) reads in a stream of characters, identifies the
154:
in linguistics. What is called "lexeme" in rule-based natural language processing can be equal to the linguistic equivalent only in
609:. These are also defined in the grammar and processed by the lexer but may be discarded (not producing any tokens) and considered
2192:
1162:
803:
Regular expressions and the finite-state machines they generate are not powerful enough to handle recursive patterns, such as "
766:
IDENTIFIER net_worth_future EQUALS OPEN_PARENTHESIS IDENTIFIER assets MINUS IDENTIFIER liabilities CLOSE_PARENTHESIS SEMICOLON
1933:
1624:
1460:
763:
might be converted into the following lexical token stream; whitespace is suppressed and special characters have no value:
1161:
page 111, "Compilers
Principles, Techniques, & Tools, 2nd Ed." (WorldCat) by Aho, Lam, Sethi and Ullman, as quoted in
2187:
1393:
Sebesta, R. W. (2006). Concepts of programming languages (Seventh edition) pp. 177. Boston: Pearson/Addison-Wesley.
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1948:
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1388:
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in C, where the token class of a sequence of characters cannot be determined until the semantic analysis phase since
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1719:
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Regular expressions compactly represent patterns that the characters in lexemes might follow. For example, for an
2136:
1789:
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240:
514:. This is necessary in order to avoid information loss in the case where numbers may also be valid identifiers.
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1784:
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generate tokens, while line continuation prevents a token from being generated even though newlines generally
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1774:
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940:, taking in a lexical specification – generally regular expressions with some markup – and emitting a lexer.
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Tokens are identified based on the specific rules of the lexer. Some methods used to identify tokens include
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113:
17:
1439:
1746:
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963:
191:
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1204:
Rethinking
Chinese Word Segmentation: Tokenization, Character Classification, or Word break Identification
570:. From there, the interpreted data may be loaded into data structures for general use, interpretation, or
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1913:
1908:
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147:
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166:. What is called a lexeme in rule-based natural language processing is more similar to what is called a
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1799:
1577:
633:, below. Secondly, in some uses of lexers, comments and whitespace must be preserved – for examples, a
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2223:
1671:
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61:-based. Second, LLM tokenizers perform a second step that converts the tokens into numerical values.
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All contiguous strings of alphabetic characters are part of one token; likewise with numbers.
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with an assigned and thus identified meaning, in contrast to the probabilistic token used in
1163:
https://stackoverflow.com/questions/14954721/what-is-the-difference-between-token-and-lexeme
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800:. This means "any character a-z, A-Z or _, followed by 0 or more of a-z, A-Z, _ or 0-9".
642:
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693:, rule). In some languages, the lexeme creation rules are more complex and may involve
649:), but this separate phase has been eliminated and these are now handled by the lexer.
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Punctuation and whitespace may or may not be included in the resulting list of tokens.
510:. The parser typically retrieves this information from the lexer and stores it in the
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53:(LLMs) but with two differences. First, lexical tokenization is usually based on a
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is also a term for the first stage of a lexer. A lexer forms the first phase of a
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1979:
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When a lexer feeds tokens to the parser, the representation used is typically an
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The lexical analysis of this expression yields the following sequence of tokens:
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is the same on both sides, unless a finite set of permissible values exists for
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or derivatives. However, lexers can sometimes include some complexity, such as
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Lexers are generally quite simple, with most of the complexity deferred to the
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1938:
1918:
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1080:: INDENT–DEDENT depend on the contextual information of prior indent levels.
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Conversion of character sequences into token sequences in computer science
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1943:
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634:
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58:
1063:(blocks determined by indenting) can be implemented in the lexer, as in
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characters, such as a space or line break, or by punctuation characters.
40:
is conversion of a text into (semantically or syntactically) meaningful
1666:
1026:
928:
parser generator, or rather some of their many reimplementations, like
49:. Lexical tokenization is related to the type of tokenization used in
1534:
933:
811:
closing parentheses." They are unable to keep count, and verify that
773:
571:
526:
292:
46:
1407:"On the applicability of the longest-match rule in lexical analysis"
590:, which defines the lexical syntax. The lexical syntax is usually a
2009:
1989:
1974:
1953:
1923:
1868:
1833:
1714:
1025:, though it is absent in B or C. Semicolon insertion is present in
861:
789:(which is plugged into template code for compiling and executing).
175:
733:
For example, in the source code of a computer program, the string
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2004:
1984:
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1101:
978:
567:
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109:
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A rule-based program, performing lexical tokenization, is called
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Tokenization is particularly difficult for languages written in
563:. If the lexer finds an invalid token, it will report an error.
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in the stream, and categorizes them into tokens. This is termed
1512:
1507:
1105:
semantic analyzer back to the lexer, which complicates design.
857:
547:, which are understood by a lexical analyzer generator such as
530:
151:
920:, and such tools often come together. The most established is
97:, which segments the input string into syntactic units called
2202:
1838:
638:
217:. The token name is a category of a rule-based lexical unit.
891:, such as Korean, also make tokenization tasks complicated.
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incorporates a lexer, which unescapes the escape sequences.
713:, which goes over the characters of the lexeme to produce a
1276:
1018:
949:
925:
167:
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opening parentheses, followed by a statement, followed by
1999:
1202:
Huang, C., Simon, P., Hsieh, S., & Prevot, L. (2007)
898:
that identifies collocations in a later processing step.
865:
641:, whitespace and comments were eliminated as part of the
621:). There are two important exceptions to this. First, in
181:
488:
the string is not implicitly segmented on spaces, as a
131:
27:"Lexer" redirects here. For people with this name, see
401:
Groups of non-printable characters. Usually discarded.
320:
Symbols that operate on arguments and produce results.
174:), although in some cases it may be more similar to a
1271:
1269:
966:, or statements into blocks, to simplify the parser.
1033:, at the start of potentially ambiguous statements.
1405:Yang, W.; Tsay, Chey-Woei; Chan, Jien-Tsai (2002).
1175:"Structure and Interpretation of Computer Programs"
1425:. NSC 86-2213-E-009-021 and NSC 86-2213-E-009-079.
1266:
349:Numeric, logical, textual, and reference literals.
101:and categorizes these into token classes, and the
822:Structure and Interpretation of Computer Programs
2215:
1685:
1221:ACM Letters on Programming Languages and Systems
105:, which converts lexemes into processed values.
1482:
1315:", golang-nuts, Rob 'Commander' Pike, 12/10/09
1468:
1214:
297:Punctuation characters and paired delimiters.
93:. Lexing can be divided into two stages: the
1411:Computer Languages, Systems & Structures
1404:
875:, which exhibit no word boundaries, such as
601:Two important common lexical categories are
521:, specific sequences of characters termed a
1215:Bumbulis, P.; Cowan, D. D. (Mar–Dec 1993).
1083:
483:The quick brown fox jumps over the lazy dog
1475:
1461:
1217:"RE2C: A more versatile scanner generator"
378:Line or block comments. Usually discarded.
170:in linguistics (not to be confused with a
1232:
1145:"Anatomy of a Compiler and The Tokenizer"
525:, specific separating characters called
188:Large language model § Tokenization
116:phases, and can often be generated by a
1368:Algorithms + Data Structures = Programs
613:, at most separating two tokens (as in
594:, with the grammar rules consisting of
456:A token name is what might be termed a
14:
2216:
996:
182:Lexical token and lexical tokenization
146:What is called "lexeme" in rule-based
64:
1456:
630:
158:, such as English, but not in highly
125:
57:, whereas LLM tokenizers are usually
1934:Simple Knowledge Organization System
1429:
1017:Semicolon insertion is a feature of
969:
117:
2234:Programming language implementation
1326:"Lexical analysis > Indentation"
1155:
956:
678:lexeme may contain any sequence of
586:often includes a set of rules, the
24:
993:, other shell scripts and Python.
936:). These generators are a form of
901:
577:
25:
2250:
1949:Thesaurus (information retrieval)
1398:
245:Names assigned by the programmer.
1048:
1002:character stream, and is termed
912:Lexers are often generated by a
864:, and larger constructs such as
645:phase (the initial phase of the
467:can be considered a sub-task of
410:Consider this expression in the
209:. A lexical token consists of a
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268:Reserved words of the language.
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150:is not equal to what is called
1530:Natural language understanding
1447:Word Mention Segmentation Task
1305:
1287:
1249:
1208:
1196:
1167:
1137:
13:
1:
2054:Optical character recognition
1423:10.1016/S0096-0551(02)00014-0
1330:The Python Language Reference
1130:
1008:automatic semicolon insertion
172:word in computer architecture
1747:Multi-document summarization
1430:Trim, Craig (Jan 23, 2013).
981:) results in the line being
828:
700:
543:Tokens are often defined by
192:tokenization (data security)
7:
2077:Latent Dirichlet allocation
2049:Natural language generation
1914:Machine-readable dictionary
1909:Linguistic Linked Open Data
1484:Natural language processing
1108:
1021:and its distant descendant
706:
671:
556:
148:natural language processing
138:Word boundary (linguistics)
10:
2255:
1829:Explicit semantic analysis
1578:Deep linguistic processing
1355:Compiling with C# and Java
1347:
1096:A more complex example is
1052:
905:
657:
652:
551:, or handcoded equivalent
221:Examples of common tokens
185:
135:
132:Disambiguation of "lexeme"
26:
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2090:
2062:
2022:
1967:
1889:
1877:
1808:
1765:
1737:
1672:Word-sense disambiguation
1548:
1525:Computational linguistics
1490:
1432:"The Art of Tokenization"
1125:List of parser generators
474:For example, in the text
383:/* Retrieves user data */
142:Word boundary (computing)
2198:Natural Language Toolkit
2122:Pronunciation assessment
2024:Automatic identification
1854:Latent semantic analysis
1810:Distributional semantics
1695:Compound-term processing
1593:Named-entity recognition
1262:3.1.2.1 Escape Character
1084:Context-sensitive lexing
985:– the following line is
938:domain-specific language
843:Tokens are separated by
666:, is usually based on a
566:Following tokenizing is
186:Not to be confused with
2102:Automated essay scoring
2072:Document classification
1739:Automatic summarization
1383:, Niklaus Wirth, 1996,
1370:, Niklaus Wirth, 1975,
889:Agglutinative languages
625:languages that delimit
582:The specification of a
1959:Universal Dependencies
1652:Terminology extraction
1635:Semantic decomposition
1630:Semantic role labeling
1620:Part-of-speech tagging
1588:Information extraction
1573:Coreference resolution
1563:Collocation extraction
783:state transition table
728:escaped string literal
414:programming language:
2229:Compiler construction
1720:Sentence segmentation
1438:. IBM. Archived from
1381:Compiler Construction
1277:"3.6.4 Documentation"
1257:Bash Reference Manual
1234:10.1145/176454.176487
1053:Further information:
721:. The evaluators for
662:The first stage, the
553:finite state automata
207:large language models
51:large language models
2172:Voice user interface
1883:datasets and corpora
1824:Document-term matrix
1677:Word-sense induction
1031:defensive semicolons
787:finite-state machine
668:finite-state machine
584:programming language
512:abstract syntax tree
464:Lexical tokenization
293:separator/punctuator
235:Sample token values
38:Lexical tokenization
2152:Interactive fiction
2082:Pachinko allocation
2039:Speech segmentation
1995:Google Ngram Viewer
1767:Machine translation
1757:Text simplification
1752:Sentence extraction
1640:Semantic similarity
1357:, Pat Terry, 2005,
1004:semicolon insertion
997:Semicolon insertion
932:(often paired with
674:). For example, an
643:line reconstruction
596:regular expressions
545:regular expressions
519:regular expressions
389:// must be negative
228:(Lexical category)
222:
160:synthetic languages
65:Rule-based programs
2162:Question answering
2034:Speech recognition
1899:Corpus linguistics
1879:Language resources
1662:Textual entailment
1645:Sentiment analysis
1071:and closing brace
924:, paired with the
498:regular expression
220:
164:fusional languages
156:analytic languages
110:syntactic analysis
2211:
2210:
2167:Virtual assistant
2092:Computer-assisted
2018:
2017:
1775:Computer-assisted
1733:
1732:
1725:Word segmentation
1687:Text segmentation
1625:Semantic analysis
1613:Syntactic parsing
1598:Ontology learning
1120:Lexical semantics
1045:generate tokens.
975:Line continuation
970:Line continuation
918:parser generators
873:scriptio continua
771:compiler-compiler
647:compiler frontend
508:semantic analysis
408:
407:
366:"music"
114:semantic analysis
83:compiler frontend
16:(Redirected from
2246:
2224:Lexical analysis
2188:Formal semantics
2137:Natural language
2044:Speech synthesis
2026:and data capture
1929:Semantic network
1904:Lexical resource
1887:
1886:
1705:Lexical analysis
1683:
1682:
1608:Semantic parsing
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1313:Semicolons in Go
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1181:. Archived from
1179:mitpress.mit.edu
1171:
1165:
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1149:www.cs.man.ac.uk
1141:
1074:
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957:Phrase structure
908:Parser generator
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739:net_worth_future
723:integer literals
631:phrase structure
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592:regular language
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460:in linguistics.
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213:and an optional
126:phrase structure
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2214:
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2156:Syntax guessing
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2117:Predictive text
2112:Grammar checker
2093:
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2058:
2025:
2014:
1980:Bank of English
1963:
1891:
1882:
1873:
1804:
1761:
1729:
1681:
1583:Distant reading
1558:Argument mining
1544:
1540:Text processing
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1436:Developer Works
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1068:
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1051:
1014:or semicolons.
1012:trailing commas
999:
972:
959:
916:, analogous to
914:lexer generator
910:
904:
902:Lexer generator
831:
779:production rule
767:
756:
753:
750:
747:
744:
741:
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703:
680:numerical digit
660:
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611:non-significant
588:lexical grammar
580:
578:Lexical grammar
538:enumerated type
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29:Lexer (surname)
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1864:Word embedding
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1844:Language model
1841:
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1821:
1815:
1813:
1806:
1805:
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1795:Transfer-based
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1617:
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1595:
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1568:Concept mining
1565:
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1554:
1552:
1546:
1545:
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1532:
1527:
1522:
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1451:
1450:
1444:
1442:on 2019-05-30.
1427:
1417:(3): 273–288.
1400:
1399:External links
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1115:Lexicalization
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42:lexical tokens
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1939:Speech corpus
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1919:Parallel text
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1710:Text chunking
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1700:Lemmatisation
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1550:Text analysis
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1491:General terms
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1449:, an analysis
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1389:0-201-40353-6
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1185:on 2012-10-30
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686:maximal munch
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18:Token scanner
2107:Concordancer
1704:
1503:Bag-of-words
1440:the original
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1354:
1333:. Retrieved
1329:
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1295:Effective Go
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1187:. Retrieved
1183:the original
1178:
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1078:context-free
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2064:Topic model
1944:Text corpus
1790:Statistical
1657:Text mining
1498:AI-complete
754:liabilities
719:unstropping
617:instead of
603:white space
232:Explanation
226:Token name
215:token value
77:, although
59:probability
2218:Categories
1785:Rule-based
1667:Truecasing
1535:Stop words
1363:032126360X
1300:Semicolons
1189:2009-03-07
1131:References
1027:JavaScript
964:statements
906:See also:
858:hyphenated
845:whitespace
561:tokenizing
527:delimiters
397:whitespace
241:identifier
211:token name
162:, such as
136:See also:
120:, notably
103:evaluating
47:data types
2094:reviewing
1892:standards
1890:Types and
983:continued
934:GNU Bison
862:emoticons
829:Obstacles
774:toolchain
711:evaluator
701:Evaluator
615:if x
572:compiling
71:tokenizer
2010:Wikidata
1990:FrameNet
1975:BabelNet
1954:Treebank
1924:PropBank
1869:Word2vec
1834:fastText
1715:Stemming
1243:14814637
1109:See also
607:comments
316:operator
176:morpheme
95:scanning
2239:Parsing
2181:Related
2147:Chatbot
2005:WordNet
1985:DBpedia
1859:Seq2seq
1603:Parsing
1518:Trigram
1348:Sources
1335:21 June
1102:typedef
1089:lexer.
979:newline
881:Chinese
860:words,
825:book).
794:English
676:integer
672:lexemes
664:scanner
658:Scanner
653:Details
568:parsing
557:lexemes
501:/\s{1}/
471:input.
469:parsing
374:comment
360:6.02e23
345:literal
264:keyword
99:lexemes
91:linters
79:scanner
75:scanner
2154:(c.f.
1812:models
1800:Neural
1513:Bigram
1508:n-gram
1387:
1374:
1361:
1241:
1065:Python
987:joined
785:for a
748:assets
707:lexeme
627:blocks
531:parser
476:string
285:return
203:string
152:lexeme
2203:spaCy
1848:large
1839:GloVe
1239:S2CID
883:, or
715:value
689:, or
639:ALGOL
279:while
253:color
201:is a
190:, or
73:, or
1968:Data
1819:BERT
1385:ISBN
1372:ISBN
1359:ISBN
1337:2023
1059:The
1019:BCPL
991:bash
950:re2c
930:flex
926:yacc
885:Thai
866:URIs
605:and
523:flag
354:true
331:<
168:word
140:and
2000:UBY
1419:doi
1298:, "
1229:doi
1039:not
1006:or
922:lex
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549:lex
503:).
496:or
494:" "
122:lex
112:or
89:or
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1268:^
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1043:do
1023:Go
887:.
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856:,
757:);
705:A
574:.
478::
404:–
386:,
363:,
357:,
334:,
328:,
307:,
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282:,
276:,
273:if
257:UP
255:,
251:,
197:A
178:.
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1073:}
1069:{
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813:n
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805:n
798:*
751:–
745:(
742:=
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438:2
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432:b
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423:=
420:x
412:C
337:=
325:+
309:;
305:(
301:}
249:x
194:.
31:.
20:)
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.