עיבוד שפות טבעיות - שיעור אחד עשר Unification (cont.) Semantics

1. עיבוד שפות טבעיות - שיעור אחד עשרUnification (cont.)Semantics עידו דגן המחלקה למדעי המחשב אוניברסיטת בר אילן 88-680

2. Feature structures in the grammar • CF grammar rules can be augmented with feature structures and with unification operations to express constraints on the constituents of a rule • An example notation (the PATR-II formalism):β 0β 1... β n {set of constraints} • Where the constraints have one of the following two forms: • < βi feature path> =(unify) atomic value • < βi feature path> =(unify) < βj feature path> • e.g.S NP VP<NP NUMBER> = <VP NUMBER> 88-680

3. Feature structures in the grammar • S  NP VP{NP AGREEMENT} = {VP AGREEMENT} • This flight serves breakfast • These flights serve breakfast • S  Aux NP VP{Aux AGREEMENT} = {NP AGREEMENT} • Does this flight serve breakfast? • Do these flights serve breakfast? 88-680

4. Feature structures in the grammar • NP  Det Nominal<Det AGREEMENT> = <Nominal AGREEMENT><NP AGREEMENT> = <Nominal AGREEMENT> • this flight vs. these flights 88-680

5. Feature structures in the grammar • Lexical constituents receive their agreement features directly from the lexicon • Aux  does<Aux AGREEMENT NUMBER> = sg<Aux AGREEMENT PERSON> = 3 • Det this<Aux AGREEMENT NUMBER> = sg • Det these<Aux AGREEMENT NUMBER> = pl 88-680

6. Feature structures in the grammar • Verb serve<Verb AGREEMENT NUMBER> = pl • Verb  serves<Verb AGREEMENT NUMBER> = sg<Verb AGREEMENT PERSON> = 3 • Non-lexical constituents(e.g. VPs) receive agreement values from their constituents • VP Verb NP<VP AGREEMENT> = <Verb AGREEMENT> 88-680

7. Feature structures in the grammar • Agreement (NP and Nominal) • Noun flight<Noun AGREEMENT NUMBER> = sg • Noun flights<Noun AGREEMENT NUMBER> = pl • Nominal Noun<Nominal AGREEMENT> = <Noun AGREEMENT> 88-680

8. Feature structures in the grammar • For most grammatical categories, the features are copied from one child to the parent • The child that provides the features is called the head of the phrase (the features are the head features) • VP  Verb NP<VP AGREEMENT> = <Verb AGREEMENT> • NP  Det Nominal<Det AGREEMENT> = <Nominal AGREEMENT><NP AGREEMENT> = <Nominal AGREEMENT> • Nominal Noun<Nominal AGREEMENT> = <Noun AGREEMENT> 88-680

9. Subcategorization • VP  Verb {VP SUBCAT} = INTRANS • VP  Verb NP {VP SUBCAT} = TRANS • VP  Verb NP NP {VP SUBCAT} = DITRANS 88-680

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11. Semantics 88-680

12. Representing Meaning • Up to now, we have not focussed at all on what things mean. • How does one represent meaning? • meaning representations:representations that bridge that gap from linguistic forms to knowledge of the world. 88-680

13. Meaning Representations • What can serve as a meaning representation... • Anything that serves the core practical purposes of a program that is doing semantic processing: • Answering questions • Determining truth • Making inferences 88-680

14. semantic analysis • הפעולה של השמת מבני משמעות לרכיבים לשוניים נקראת ניתוח סמנטי semantic analysis 88-680

15. The architecture Sentence Syntactic Structure Parsing Parse Tree Semantic Analysis Semantic Representation 88-680

16. Applications • Ideally, applications would interact with the semantic representation of texts. • Need a representation which is unambiguous, even though the text is ambiguous. 88-680

17. Problem of Ambiguity • אין התאמה חח“ע בין משפט ובין הייצוג הסמנטי שלו (רב משמעות) וגם לא להפך: 88-680

18. Canonical Form • Inputs that mean the same thing should have the same meaning representation! • ייצוג משמעות אחיד לקלטים שונים נקרא canonical form • מאפשר פישוט של תהליך ההסקה, אם המערכת צריכה להתמודד רק עם ייצוג יחיד. 88-680

19. Canonical Form • John gave a car to Mary • Mary was given a car by John • give(John, Mary, a car) • ∃x ∃y : give(x) ∧ agent(x, John) ∧ recipient(x,Mary) ∧ object(x,y) ∧ car(y) 88-680

20. Example • Every bird lives on a tree • Quantifier and word sense ambiguities. • ∀x bird(x) ∃y : tree1(y) ∧ live_ on(x,y) • ∃y tree1(y): ∀x bird(x)  live_on(x,y) 88-680

21. FOPC – First Order Predicate Logic • Meaning Structure of Language • Choice of FOPC isn't completely arbitrary or driven by the needs of applications... • Human languages: • display a basic predicate-argument structure • make use of variables • make use of quantifers • display a partially compositional semantics 88-680

22. Predicate Argument Structure • בכל השפות המדוברות מבנה הפרדיקט-ארגומנטים הוא הגרעין והיסוד של המשמעות בשפה. • ההנחה הגסה הראשונה היא, אם כן, שבמשפט קיים יחס כלשהו בין המושגים המובעים במילים שבו וברכיביו השונים. • אותו יחס הוא הבסיס לייצוג הסמנטי שיש למשפט. • אחד התפקידים החשובים של התחביר הוא לתת מסגרת שתאפשר בנייה של אותו ייצוג, והייצוג צריך שיהיה לו קשר הדוק עם אותו מבנה תחבירי. 88-680

23. Predicate Argument Structure • Predicate-like elements: • Verbs, VPs, prepositions, adjectives, some nouns. • Argument-like elements: • Nouns, Nominals, NPs, etc 88-680

24. Example • “John gave Mary the book” • Giving(John, Mary, book) • ∃x,y: Giving(x)∧ Giver(John,x) ∧ Givee(Mary,x) ∧ Given(y,x) ∧ Book(y) 88-680

25. Semantic Analysis • Syntax driven semantics: • Most methods rely in some way on a prior or concurrent syntactic analysis (parse). • Compositional Semantics: • at the core of most methods is the principle of compositionality which states that the meaning of the whole is based on the meaning of parts. • What are the parts? Words and syntactic constructs 88-680

26. Augmented CF Rules • Verb  serves{x y ∃e IsA(e, serving) ∧ server(e,y) ∧ served(e,x) } • S  NP VP {VP.sem(NP.sem) } • VP  Verb NP {Verb.sem(NP.sem)} • MassNoun  Meat {Meat} • ProperNoun  AyCaramba {AyCaramba} 88-680

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29. Determiners 88-680

30. Issues • How to represent tense and aspect? • Idioms (are not compositional)! • Pragmatics (extra-syntactic)! • Inference • And much more… 88-680

31. Ambiguity Resolution • Still have problem of ambiguity resolution: • Word sense disambiguation • Attachment ambiguity resolution 88-680

32. WSD • Problem: many words have different meanings or senses, i.e., there is ambiguity about how they are to be specifically interpreted (e.g., differentiate). • Task: to determine which of the senses of an ambiguous word is invoked in a particular use of the word by looking at the context of its use. 88-680

33. Word Senses • Bank • The rising ground bordering a lake, river, or sea • An establishment for the custody, loan exchange, or issue of money, for the extension of credit, and for facilitating the transmission of funds • Title • Name/heading of a book, statue, work of art or music, etc. • Material at the start of a film • The right of legal ownership (of land) • The document that is evidence of the right • An appellation of respect attached to a person’s name • A written work (synecdoche: part stands for the whole) 88-680

34. Solution • look at the words around an ambiguous word in a large context window. Each word contributes potentially useful information about which sense of the ambiguous word is likely to be used with it. • Apply a supervised learning mechanism 88-680

35. WordNet • on-line lexical reference system, linguistically motivated • nouns, verbs, adjectives, adverbs are organized into synonym sets (lexical concepts), • contains a taxonomy of about 100,000 words 88-680

36. WordNet • semantic relationships represented • Synonymy: similarity of work meaning, concepts as sets of words • Antonymy: dichotomy in meaning of words, used for organizing adjectives and adverbs • Hyponymy:IS-A relationships Superconcept to Subconcepts • Meronymy: Part-Of relationships between concepts • Morphological Relationships: used to reduce word forms 88-680