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Computational Semantics

Computational Semantics. Torbjörn Lager Department of Linguistics Uppsala University. Computational Semantics. Compositional, logical semantics* Computational lexical semantics Word sense disambiguation* Text categorization Information extraction Information retrieval.

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Computational Semantics

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  1. Computational Semantics Torbjörn Lager Department of Linguistics Uppsala University

  2. Computational Semantics • Compositional, logical semantics* • Computational lexical semantics • Word sense disambiguation* • Text categorization • Information extraction • Information retrieval NLP1 - Torbjörn Lager

  3. Logical Semantics Example • John laughed • laughed'(j) • Nobody laughed • x[laughed'(x)] • But this is just translation! What's semantic about that? NLP1 - Torbjörn Lager

  4. What Is the Name of This Business? • Truth conditional semantics • Model theoretic semantics • Logical semantics • Formal semantics • Compositional semantics • Syntax-driven semantic analysis • Compositional, logical, truth conditional, model theoretic semantics .... NLP1 - Torbjörn Lager

  5. We use language to talk about the world Semantics is something that relates sentences (or utterances) of language and the outside world There are other ideas about meaning, but in this tradition we don't believe in them! An Important Tradition Natural language The outside world NLP1 - Torbjörn Lager

  6. Meaning = Truth conditions Examples: "John whistles" is true iff John whistles "John visslar" is true iff John whistles "Ogul fautu seq" is true iff... Truth Conditional Semantics Natural language The outside world NLP1 - Torbjörn Lager

  7. We don't know what the world is really like, so let's talk about a model of the world instead Such a model does (usually) consists of individuals, sets of individuals, functions and relations. i.e the sort of things set theory talks about Truth becomes truth relative to a model Model Theoretic Semantics Natural language Model The outside world NLP1 - Torbjörn Lager

  8. The Compositionality Principle: The meaning of the whole is a function of the meaning of the parts and the mode of combining them. The meaning of a complex expression is uniquely determined by the meaning of its constituents and the syntactic construction used to combine them. Compositional Semantics Natural language Model The World NLP1 - Torbjörn Lager

  9. A simple model M: Domain: {John, Mary, Paul} Interpretation: Names: "John" refers to John , "Mary" refers to Mary, etc. Verbs: "whistles" refers to {John, Paul} Example "John whistles" is true in M iff the individual in M referred to as "John" is an element in the set of individuals that "whistles" refer to. Truth Conditional, Model Theoretic and Compositional Semantics Combined Richard Montague (1970): "I reject the contention that an important theoretical difference exists between formal and natural languages" NLP1 - Torbjörn Lager

  10. Account for the meanings of natural language utterances by translating them into another language. It could be any language, but only if this language has a formal semantics are we done. Translational Semantics Natural language Logical Form Language Model The World NLP1 - Torbjörn Lager

  11. Why Logical Semantics? • Account for ambiguity • "every man loves a woman" • Allow evaluation • e.g. by database lookup • Allow logical inference • Every man who whistles is happy • John is a man • John whistles • Therefore: John is happy NLP1 - Torbjörn Lager

  12. Applications of Logical Semantics • NLU systems • Semantics + 'World knowledge' --> 'understanding' • Information Extraction • Machine translation • LF as (part of an) interlingua • Dialogue Systems NLP1 - Torbjörn Lager

  13. Have same truth conditions Grammar and Logical Form • S -> NP VP[S] = [VP]([NP]) • NP -> john[NP] = j • VP -> whistles[VP] = x[whistles'(x)] • [john whistles] = whistles'(j) cf. The principle of compositionality NLP1 - Torbjörn Lager

  14. Beta Reduction (Lambda Conversion) • [S] = [VP]([NP]) • [NP] = j • [VP] = x[whistles'(x)] • Beta reduction rule: u()   where every occurrence of u in  is replaced by • x[whistles'(x)](j) application • whistles'(j) reduction NLP1 - Torbjörn Lager

  15. Grammar and Logical Form • S -> NP VP[S] = [NP]([VP]) • NP -> john[NP] = P[P(j)] • VP -> whistles[VP] = x[whistles'(x)] • [john whistles] = whistles'(j) NLP1 - Torbjörn Lager

  16. From Logical Form to Truth Conditions • whistles'(j) is true iffthe individual (in the model) denoted by 'j' has the property denoted by 'whistles' • cf. "John whistles" is true iff John whistles NLP1 - Torbjörn Lager

  17. Beta Reduction (Lambda Conversion) • [S] = [NP]([VP]) • [NP] = P[P(j)] • [VP] = x[whistles'(x)] • Beta reduction rule: u()   where every occurrence of u in  is replaced by • P[P(j)](x[whistles'(x)]) application • x[whistles'(x)](j) reduction • whistles'(j) reduction NLP1 - Torbjörn Lager

  18. A Larger Example • S -> NP VP [S] = [NP]([VP]) • NP -> DET N [NP] = [DET]([N]) • DET -> every[DET] = Q[P[z[Q(z)  P(z)]]] • N -> man [N] = x[man'(x)] • VP -> whistles[VP] = x[whistles'(x)] • [every man whistles} = • z[man'(z)  whistles'(z)] NLP1 - Torbjörn Lager

  19. A Larger Example (cont'd) • [S] = [NP]([VP]) • [NP] = [DET]([N]) • [DET] = Q[P[z[Q(z)  P(z)]]] • [N] = x[man'(x)] • [VP] = x[whistles'(x)] • Q[P[z[Q(z)  P(z)]]](x[man'(x)]) application • P[z[x[man'(x)](z)  P(z)]] reduction • P[z[man'(z)  P(z)]] reduction • P[z[man'(z)  P(z)]](x[whistles'(x)]) application • z[man'(z) x[whistles'(x)](z)] reduction • z[man'(z)  whistles'(z)] reduction NLP1 - Torbjörn Lager

  20. Alternative Ontologies • [john whistles] = ex[isa(e,WhistleEvent)  agent(e,x)  named(x,"john")] • [john whistles] = whistles'(j) NLP1 - Torbjörn Lager

  21. Alternative Meaning Representation Formalisms Discourse Representation Theory • [every man whistles} = x man(x) whistles(x) NLP1 - Torbjörn Lager

  22. Semantics Research • How to design a nice meaning representation language? • Ontology? • How to treat a particular NL construct? • in a compositional way? • and end up with correct truth conditions? • and still be elegant and clean? • How to deal with things like time and events, propositional attitude reports, reference to non-existent individuals, etc.? • How to solve a particular semantic puzzle? • How to design a nice syntax-semantics interface? • How to design a nice semantics-pragmatics interface? • What is the role of inference in semantic processing? • How to account for things like presuppositions? NLP1 - Torbjörn Lager

  23. Semantic Puzzles • Why is "Every man loves a woman" ambiguous, but not "Every man loves Mary"? • What's wrong with the following argument: "Nothing is better than a long and prosperous life. A ham sandwich is better than nothing. Therefore, a ham sandwich is better than a long and prosperous life." • The morning star = the evening star, still"John believes that Venus is the morning star" may be true, and at the same time, "John believes that Venus is the evening star", may be false. • Everything written on this slide is false. NLP1 - Torbjörn Lager

  24. Word Sense Disambiguation Torbjörn Lager Department of Linguistics Uppsala University

  25. Example: Senses of "interest" • From the LDOCE • readiness to give attention, • quality of causing attention to be given • activity, subject, etc., which one gives time and attention to • advantage, advancement, or favour • a share (in a company, business, etc.) • money paid for the use of money NLP1 - Torbjörn Lager

  26. WSD Examples At the same time, the drop in interest rates since the spring has failed to revive the residential construction industry. Cray Research will retain a 10% interest in the new company, which will be based in Colorado Springs. Although that may sound like an arcane maneuver of little interest outside Washington, it would set off a political earthquake. NLP1 - Torbjörn Lager

  27. Why Word Sense Disambiguation? • Well, compositional logical semantics doesn't deal with word meaning, so... • Machine translation • A non-disambiguated Russian translation of: “The spirit is willing but the flesh is weak” gave “The vodka is good but the meat is rotten” • Information Retrieval • When searching the web for info about companies buying shares in other companies, you don’t want to retrieve the information about interest rates. • Provide clues to pronounciation • "banan" -> BAnan or baNAN NLP1 - Torbjörn Lager

  28. Approaches to WSD • Deep (but brittle) WSD • 'Selectional restriction'-based approaches • Approaches based on general reasoning with 'world knowledge' • Shallow and robust WSD • Machine learning approaches • Supervised learning • Unsupervised learning • Bootstrapping approaches • Dictionary-based approaches • Various combinations of methods NLP1 - Torbjörn Lager

  29. Machine Learning Approaches • Training data in the form of annotated corpora • Decide on features on which to condition • Preprocessing Steps • Context trimming • Stemming/Lemmatizing • Part-of-speech tagging • Partial Parsing • Use a machine learning algorithm • Enter the training-test cycle NLP1 - Torbjörn Lager

  30. Dictionary-Based Approaches • Lesk (1986) • Find the dictionary definition that overlaps most with the definitions for the words in the ambiguous word’s context. • Problem 1: A lot of computation. • Problem 2: Definitions are usually too short NLP1 - Torbjörn Lager

  31. Lesk Example • Lexicon entries: pine 1 kinds of evergreen tree with needle-shaped leaves 2 waste away through sorrow or illness cone 1 solid body which narrows to a point 2 something of this shape whether solid or hollow 3 fruit of certain evergreen tree • Example: .... pine cone ... • The third sense for "cone" is selected here, since two of the (content) words in its entry, "evergreen" and "tree", overlap with words in the entry for "pine", and this doesn't happen for the other senses. NLP1 - Torbjörn Lager

  32. SENSEVAL-2: Second International Workshop on Evaluating Word Sense Disambiguation Systems • The purpose of SENSEVAL is to evaluate the strengths and weaknesses of WSD programs with respect to different words, different varieties of language, and different languages. • Two types of task over 12 languages. • In the "all-words" task, the evaluation is on almost all of the content words in a sample of texts. • In the "lexical sample" task, first we sample the lexicon, then we find instances in context of the sample words and the evaluation is on those instances only. • All-words: Czech, Dutch, English, Estonian • Lexical sample: Basque, Chinese, Danish, English, Italian, Japanese, Korean, Spanish, Swedish • About 35 teams participated, submitting over 90 systems. NLP1 - Torbjörn Lager

  33. The Swedish Lexical Sample Task • 40 lemmas: 20 nouns, 15 verbs and 5 adjectives. • Together representing 145 senses and 304 sub-senses. • 8,718 annotated instances were provided as training material • 1,527 unannotated instances were provided for testing. • A lexicon - the GLDB (Gothenburg Lexical Database) - complete with morphological information, definitions, language examples, etc., was also available. NLP1 - Torbjörn Lager

  34. Demo • http://www.ling.gu.se/~lager/Home/pwe_ui.html NLP1 - Torbjörn Lager

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