Alessandro Mazzei MASTER DI SCIENZE COGNITIVE GENOVA 2005

Transcription

1 Alessandro Mazzei Dipartimento di Informatica Università di Torino MATER DI CIENZE COGNITIVE GENOVA Natural Language Grammars and Parsing

2 Natural Language yntax Paolo ama Francesca yntactic Parsing: deriving a syntactic structure from the word sequence NP VP N V N Paolo ama Francesca sub ama obj Paolo Francesca

3 yntax and emantics Paolo ama Francesca Francesca ama Paolo yntactic Parsing yntactic Parsing NP VP N V N Paolo ama Francesca NP VP N V Francesca ama N Paolo

4 Dependency and PCFG ummary Dependency relations Dependency grammars and parsers Lexicalized PCFG

5 Anatomy of a Parser (1) Grammar Context-Free,... (2) Algorithm I. earch strategy top-down, bottom-up, left-to-right,... II.Memory organization (3) Oracle back-tracking, dynamic programming,... Probabilistic, rule-based,...

6 Generative Grammars and Natural Languages Generative Grammars can model the natural language as a formal language The derivation tree can model the syntactic structure of the sentences

7 Generative grammar G=(Σ,V,,P) Σ = alphabet V = {A,B,...} V P = {Ψ θ,...}

8 Grammar 3 G 4 =(Σ 4,{,NP,VP,V 1,V 2 },,P 4 }) Σ 4 = {I,Anna,John,Harry,saw,see,swimming} P 4 = { NP VP, VP V 1, VP V 2, NP I John Harry Anna, V 1 saw see, V 2 swimming}

9 Grammar 3 Derivation NP VP VP V 1 VP V 2 NP I John Harry Anna V 1 saw see V 2 swimming

10 Grammar 3 Derivation NP VP VP V 1 NP VP VP V 2 NP I John Harry Anna V 1 saw see V 2 swimming NP VP

11 Grammar 3 Derivation NP VP VP V 1 NP VP I VP VP V 2 NP I John Harry Anna V 1 saw see V 2 swimming NP I VP

12 Grammar 3 Derivation NP VP VP V 1 NP VP I VP I V 1 VP V 2 NP I John Harry Anna V 1 saw see V 2 swimming NP I V 1 VP

13 Grammar 3 Derivation NP VP VP V 1 VP V 2 NP I John Harry Anna V 1 saw see V 2 swimming NP VP I VP I V 1 I saw NP I V 1 saw VP

14 Grammar 3 Derivation NP VP VP V 1 VP V 2 NP I John Harry Anna V 1 saw see V 2 swimming NP VP I VP I V 1 I saw I saw NP VP NP I V 1 VP saw NP VP

15 Grammar 3 Derivation NP VP VP V 1 VP V 2 NP I John Harry Anna V 1 saw see V 2 swimming NP VP I VP I V 1 I saw I saw NP VP I saw Harry VP NP I V 1 VP saw NP Harry VP

16 Grammar 3 Derivation NP VP VP V 1 VP V 2 NP I John Harry Anna V 1 saw see V 2 swimming NP VP I VP I V 1 I saw I saw NP VP I saw Harry VP I saw Harry V 2 NP I V 1 VP saw NP Harry VP V 2

17 Grammar 3 Derivation NP VP VP V 1 VP V 2 NP I John Harry Anna V 1 saw see V 2 swimming NP VP I VP I V 1 I saw I saw NP VP I saw Harry VP I saw Harry V 2 I saw Harry swimming NP I V 1 VP saw NP Harry VP V 2 swimming

18 Dependency and PCFG ummary Dependency relations Dependency grammars and parsers Lexicalized PCFG

19 A different syntactic structure: Dependency Constituent structure represents the grouping relations among the words Dependence structure represents the dependency relations among the words NP VP N V N Paolo ama Francesca sub ama obj Paolo Francesca

20 Dependency relation Relation among two words: Head: dominant word Dependent: dominated word The head selects his dependents and determines their properties Example: the verb determines the number of his arguments

21 Dependency relation Head: dominant word ama Paolo Francesca

22 Dependency relation Dependent: dominated word ama Paolo Francesca

23 Dependency relation Dependent argument arg ama arg Paolo Francesca

24 Dependency relation Dependent argument modifier arg Paolo corre mod velocemente

25 Dependency relation Dependent argument modifier mod il cane mod giallo

26 Constituency and Dependency Constituency relation captures dependency relation in the X-bar theory X'' arg head X' X' arg mod NP Paolo VP VP V N ama Francesca ADV dolcemente

27 Constituency and Dependency Constituency relation captures dependency relation in the X-bar theory X'' arg head X' X' arg mod NP Paolo VP VP V N ama Francesca ADV dolcemente Problem with free-word order languages

28 Constituency and Dependency sub Paolo obj ama Francesca mod dolcemente NP Paolo VP VP V N ama Francesca ADV dolcemente

29 Constituency and Dependency sub Paolo :1 obj ama :2 Francesca :3 mod dolcemente :4 NP Paolo VP VP V N ama Francesca ADV dolcemente

30 Turin University Treebank Dependency Treebank: 1800 sentences, ~40000 words Various genres: newspaper, civil law, albanian, miscellaneous Augmented Relational tructure (AR) Morpho-syntactic yntactic-functional emantic

31 Turin University Treebank ************** FRAE ALB-4 ************** 1 Il (IL ART DEF M ING) [5;VERB-UBJ] 2 Governo (GOVERNO NOUN COMMON M ING) [1;DET+DEF-ARG] 3 di (DI PREP MONO) [2;PREP-RMOD] 4 Berisha ( Berisha NOUN PROPER) [3;PREP-ARG] 5 appare (APPARIRE VERB MAIN IND PRE INTRAN 3 ING) [0;TOP-VERB] 6 in (IN PREP MONO) [5;VERB-PREDCOMPL+UBJ] 7 difficolta' ( difficolta` NOUN COMMON F ALLVAL) [6;PREP-ARG] 8. (#\. PUNCT) [5;END]

32 Turin University Treebank

33 Generative Grammars and Natural Languages Generative Grammars model the generation of the sentences The derivation tree can model the constituency structure of the sentences

34 Generative Grammars and Natural Languages Generative Grammars model the generation of the sentences The derivation tree can model the constituency structure of the sentences Representation vs. Generation

35 Dependency and PCFG ummary Dependency relations Dependency grammars and parsers Lexicalized PCFG

36 Dependency grammars and parsers How can we generate a dependency structure? dependency grammar How can we build the dependency structure of a sentence? dependency parser

37 Dependency grammars In the constituency paradigm: generative grammars rewriting rule In the dependency paradigm: constraint grammars constraint

38 Dependency parsers: Turin University Parser A rule-based dependency parser that uses subcategorization frames Chunk parser (~bottom-up) AR annotation Morpho-syntactic yntactic-functional emantic

39 Turin University Parser 1) Non verbal Rules: (ADJ-QUALIF BEFORE (ADV (TYPE MANNER)) ADVMOD-MANNER ) If an adverb of subcategory (TYPE) MANNER immediately precedes a qualificative adjective, then it can depend from it via an arc labelled as ADVMOD-MANNER.... davvero veloce... veloce davvero ADVMOD-MANNER

40 Turin University Parser 2) Verbal Rules based on a taxonomy of subcategorization classes: VERB TRAN... INTRAN... INTRAN-INDOBJ-PRED (Ex. La casa gli sembra bella )...

41 Turin University Parser Paolo è davvero veloce 1) NVR Paolo è veloce ADVMOD-MANNER davvero 2) VR VERB-UBJ è VERB-PREDCOMPL Paolo veloce ADVMOD-MANNER davvero

42 Anatomy of the TUP (1) Grammar Dependency grammar (constraint),... (2) Algorithm I. earch strategy top-down, ~bottom-up, left-to-right,... II.Memory organization (3) Oracle depth-first, back-tracking, dynamic programming,... Probabilistic, rule-based,...

43 Dependency and PCFG ummary Dependency relations Dependency grammars and parsers Lexicalized PCFG

44 Probabilistic CFG G=(Σ,V,,P) A β [p] p (0,1)

45 PCFG P(T a ) =.15 *.4 *.05 *.05 *.35 *.75 *.4 *.4 *.4 *.3 *.4 *.5 = = 1.5 x 10-6 P(T b ) =.15 *.4 *.4 *.05 *.05 *.75 *.4 *.4 *.4 *.3 *.4 *.5 = = 1.7 x 10-6

46 Problem with PCFG Independence assumption: no structural and lexical preferences

47 Problem with PCFG Independence assumption: no structural and lexical preferences

48 Problem with PCFG Independence assumption: no structural and lexical preferences

49 Lexicalized PCFG Each CF rule is augmented with information about the heads of the constituents involved A BC A(head A ) B(head B ) C(head C ) Middle point between dependency and constituency paradigm

50 Lexicalized PCFG VP VBD NP PP VP(dumped) VBD(dumped) NP(sacks) PP(into) [3x10-10 ] VP(dumped) VBD(dumped) NP(cats) PP(into) [8x10-11 ] VP(dumped) VBD(dumped) NP(hats) PP(into) [4x10-10 ] VP(dumped) VBD(dumped) NP(sacks) PP(above) [1x10-12 ]

51 Lexicalized PCFG

52 Lexicalized PCFG

53 Conclusions yntactic structure: constituency and dependency relations Parsing: generative and constraint paradigm Lexicalized Probabilistic CFGs Treebank

54 References PEECH and LANGUAGE PROCEING D. Jurafsky and J.H. Martin Prentice Hall 2000 An Introduction to yntax R.D. Van Valin Cambridge 2001 TUT and TUP: