1 / 69

Semantics

Semantics. Ling 571 Fei Xia Week 6: 11/1-11/3/05. Outline. Meaning representation: what formal structures should be used to represent the meaning of a sentence? Semantic analysis: how to form the formal structures from smaller pieces? Lexical semantics: . Meaning representation.

Samuel
Download Presentation

Semantics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Semantics Ling 571 Fei Xia Week 6: 11/1-11/3/05

  2. Outline • Meaning representation: what formal structures should be used to represent the meaning of a sentence? • Semantic analysis: how to form the formal structures from smaller pieces? • Lexical semantics:

  3. Meaning representation

  4. Meaning representation • Requirements that meaning representations should fulfill • Types of meaning representation: • First order predicate calculus (FOPC) • Frame-based representation • Semantic network • Conceptual dependency diagram

  5. Requirements • Verifiability • Unambiguous representations • Canonical form • Inference • Expressiveness

  6. Verifiability • A system's ability to compare the state of affairs described by a representation to the state of affairs in some world as modeled in a knowledge base • Example: • Sent: Maharani serves vegetarian dishes. • Question: Is the statement true?

  7. Unambiguous representation • Representations should have a single unambiguous interpretation. • Example: • Mary and John bought a book • Two students met three teachers • A German teacher • A Chinese restaurant • A Canadian restaurant

  8. Canonical form • Sentences with the same thing should have the same meaning representation • Example: • Alternations: active/passive, dative shift • Does Maharani have vegetarian dishes? • Do they serve vegetarian food at Maharani?

  9. Inference • a system's ability to draw valid conclusions based on the meaning representation of inputs and its store of background knowledge. • Example: • Sent: Maharani serves vegetarian dishes • Question: can vegetarians eat at Maharani?

  10. Expressiveness • A system should be expressive enough to handle an extremely wide range of subject matter. • Example: • Belief: I think that he is smart. • Hypothetical statement: If I were you, I would buy that book. • Former president, fake ID, allegedly, apprarently

  11. Meaning representation • Requirements • Verifiability • Unambiguous representations • Canonical form • Inference • Expressiveness • Types of meaning representation: • First order predicate calculus (FOPC) • Frame-based representation • Semantic network • Conceptual dependency diagram

  12. FOPC • Elements of FOPC • Representing • Categories • Events • Time (including tense) • Aspect • Belief • …

  13. Elements of FOPC • Terms: • Constant: specific objects in the world: e.g., Maharani • Variable: a particular unknown object or an arbitrary object: e.g., a restaurant • Function: concepts: e.g., LocationOf(Maharani) • Predicates: referring to relations that hold among objects: • Ex: Serve(Maharani, food) • Arguments of predicates must be terms.

  14. Elements of FOPC (cont) • Logical connectives: • Quantifier: • Example: All restaurants serve food.

  15. Inference rules • Modus ponens: • Conjunction: • Disjunction: • Simplification: • ….

  16. FOPC • Elements of FOPC • Representing • Categories • Events • Time • Aspect • Belief • …

  17. Representing time • Past perfect: I had arrived in NY • Simple past: I arrived in NY • Present perfect: I have arrived in NY • Present: I arrive in NY • Simple future: I will arrive in NY • Future perfect: I will have arrived in NY

  18. Representing time (cont) • Reichenbach’s approach • E: the time of the event • U: the time of the utterance • R: the reference point • Example: • Past perfect: I had arrived: E > R > U • Simple past: I arrived: E=R > U • Present perfect: I have arrived: E > R=U

  19. Aspect • Four types of event expression: • Stative: I like books. I have a ticket • Activity: She drove a Mazda. I live in NY • Accomplishment: Sally booked her flight. • Achievement: He reached NY. • Differences: • Being in a state or not • occurring at a given time, or over some span of a time • Resulting in a state: happening in an instant or not.

  20. Distinguishing four types • Allowing progressive, imperative • *I am liking books. • *Like books. • Modified by in-phrase, for-phrase: in a month, for a mont • He lived in NY for five years. • *He reached NY for five minutes.

  21. Distinguishing four types (cont) • “Stop” test: stop doing something • *He stopped reaching NY. • He stopped booking the ticket • Modified by adverbs such as “deliberately”, “carefully” • *He likes books deliberately

  22. Representing beliefs • John believes that Mary ate lunch. • One possibility: • Another possibility:

  23. Representing beliefs (cont) • Substitution does not work • Example: • John knows Flight 1045 is delayed • Mary is on Flight 1045 • Does John know that Mary’s flight was delayed? FOPC is not sufficient. Use modal logic

  24. Summary of meaning representation • Five requirements: • Verifiability • Unambiguous representations • Canonical form • Inference • Expressiveness • Four types of representations: • First order predicate calculus (FOPC) • Frame-based representation • Semantic network • Conceptual dependency diagram

  25. Outline • Meaning representation: • Semantic analysis: how to form the formal structures from smaller pieces? • Lexical semantics:

  26. Semantic analysis

  27. Semantic analysis • Goal: to form the formal structures from smaller pieces • Three approaches: • Syntax-driven semantic analysis • Semantic grammars • Information extraction: filling templates

  28. Syntax-driven approach • Parsing then semantic analysis, or parsing with semantic analysis. • Semantic augmentations to grammars (e.g., CFG or LTAG) • Associate FOPC expression with lexical items • Use • Use complex-terms

  29. Sentence: AyCaramba serves meat • Goal: • Augmented rules:

  30. Quantifiers • Sentence: A restaurant serves meat • Goal: • Augmented rules:

  31. Complex terms • Current formula: • Goal: • What is needed:

  32. Quantifier scoping • Sentence: Every restaurant has a menu • Formula with complex terms • Reading 1: • Reading 2:

  33. Semantic analysis • Goal: to form the formal structures from smaller pieces • Three approaches: • Syntax-driven semantic analysis • Semantic grammar • Information extraction: filling templates

  34. Semantic grammar • Syntactic parse trees only contain parts that are unimportant in semantic processing. • Ex: Mary wants to go to eat some Italian food • Rules in a semantic grammar • InfoRequest USER want to go to eat FOODTYPE • FOODTYPENATIONALITY FOODTYPE • NATIONALITYItalian/Mexican/….

  35. Semantic grammar (cont) Pros: • No need for syntactic parsing • Focus on relevant info • Semantic grammar helps to disambiguate Cons: • The grammar is domain-specific.

  36. Information extraction • The desired knowledge can be described by a relatively simple and fixed template. • Only a small part of the info in the text is relevant for filling the template. • No full parsing is needed: chunking, NE tagging, pattern matching, … • IE is a big field: e.g., MUC. KnowItAll

  37. Summary of semantic analysis • Goal: to form the formal structures from smaller pieces • Three approaches: • Syntax-driven semantic analysis • Semantic grammar • Information extraction

  38. Outline • Meaning representation • Semantic analysis • Lexical semantics

  39. Lexical semantics

  40. What is lexical semantics? • Meaning of word: word senses • Relations among words: • Predicate-argument structures • Thematic roles • Selectional restrictions • Mapping from conceptual structures to grammatical functions • Word classes and alternations

  41. Important resources • Dictionaries • Ontology and taxonomy • WordNet • FrameNet • PropBank • Levin’s English verb classes • ….

  42. Meaning of words • Lexeme is an entry in the lexicon that includes • Orthographic form • Phonological form • Sense: lexeme’s meaning

  43. Relations among lexemes • Homonyms: same orth. and phon. forms, but different, unrelated meanings • bank vs. bank • Homophones: same phon. different orth • read vs. red, to, two, and too. • Homographs: same orth, different phon. • bass vs. bass

  44. Polysemy • Word with multiple but related meanings • He served his time in prison • He served as U.N. ambassador • They rarely served lunch after 3pm. • What’s the difference between polysemy and homonymy: • Homonymy: distinct, unrelatedmeanings • Polysemy: distinct but related meanings • How to decide: etymology, notion of coincidence

  45. Synonymy • Different lexemes with the same meaning • Substitutable in some environment: • How big is that plane? • How large is that plane? • What influences substitutablity? • Polysemy: big brother vs. large brother • Subtle shade of meaning: first class fare/?price • Colllocational constraints: big/?large mistake • Register: social factors

  46. Hyponymy • General: hypernym • “vehicle” is a hypernym of “car” • Specific: hyponym • “car” is a hyponym of “vehicle”. • Test: X is a car implies that X is a vehicle.

  47. Ontology and taxonomy • Ontology: • It is a specification of a conceptualization of a knowledge domain • It is a controlledvocabulary that describes objects and the relations between them in a formal way, and has strict rules about how to specify terms and relationships. • Taxonomy: • A taxonomy is a hierarchical data structure or a type of classification schema made up of classes, where a child of a taxonomy node represents a more restricted, smaller, subclass than its parent. • a particular arrangement of the elements of an ontology into a tree-like class inclusion structure.

  48. WordNet • Most widely used lexical database for English • Developed by George Miller etc. at Princeton • Three databases: Noun, Verb, Adj/Adv • Each entry in a database: a unique orthographic form + a set of senses • Synset: a set of synonyms • http://www.cogsci.princeton.edu/~wn

  49. WordNet (cont) • Nouns: • Hypernym: meal, lunch • Has-Member: crew, pilot • Has-part: table, leg • Antonym: leader, follower • Verbs: • Hypernym: travel, fly • Entail: snoresleep • Antonym: increase  decrease • Adj/Adv: • Antonym: heavy light, quickly slowly

  50. Lexical semantics • Meaning of word: word senses • Relations among words: • Predicate-argument structures • Thematic roles • Selectional restrictions • Mapping from conceptual structures to grammatical functions • Word classes and alternations

More Related