Building deep dependency structures with a wide-coverage CCG parser

1. Building deep dependency structures with a wide-coverage CCG parser Stephen Clark ACL2002 발표자 : 박 경 미

2. 목차 • abstract • 1 introduction • 2 the grammar • 3 the probability model • 3.1 estimating the dependency probabilities • 4 the parser • 5 experiments • 6 results • 7 conclusions and further work

3. abstract • To derive dependency structures • Describes a wide-coverage statistical parser that uses Combinatory Categorial Grammar (CCG) • In capturing the long-range dependencies • A CCG parser differs from most existing wide-coverage tree-bank parsers • A set of dependency structures used for training and testing the parser • Is obtained from a treebank of CCG normal-form • Have been derived (semi-) automatically from the Penn Treebank

4. 1. introduction • Models based on lexical dependencies • The dependencies are typically derived from a context-free phrase structure tree • Using simple head percolation heuristics • Does not work well for the long-range dependencies • CCG • “mildly context-sensitive” formalism • Provides the most linguistically satisfactory account of the dependencies • Is to facilitate recovery of unbounded dependencies

5. 1. introduction • CCG is unlike other formalisms • In that the standard predicate-argument relations can be derived via non-standard surface derivations • Non-standard surface derivations • impacts on how best to define a probability model for CCG • the “spurious ambiguity” of CCG derivations may lead to an exponential number of derivations • Some of the spurious derivations may not be present in the training data • One solution • is to consider only the normal-form derivation

6. 1. introduction • Another problem with the non-standard surface derivations • Is that the standard PARSEVAL performance measures over such derivations are uninformative • Lin(1995) and Carroll et al.(1998) • Propose recovery of head-dependencies characterising predicate-argument relations as a meaningful measure • The training and testing material for CCG parser • Is a treebank of dependency structures • Have been derived from a set of CCG derivations developed for use with another (normal-form) CCG parse

7. 2 the grammar • 결합 범주 문법(어휘 사전, 구문 범주 형태) • identify a lexical item as either a functor or argument • For the functors • The category specifies the type and directionality of the arguments and the type of the result • Ex) the category for the transitive verb bought • The slash determines that the argument is respectively to the right (/) or to the left (＼) of the functor • Its first argument as a noun phrase (NP) to its right • Its second argument as an NP to its left • Its result as a sentence

8. 2 the grammar • Extend CCG categories • To express category features, and head-word and dependency information • The feasure [dcl] specifies the category’s S result as a declarative sentence • bought identifies its head • The numbers denote dependency relations

9. 2 the grammar • Using a small set of typed combinatory rules • Derivation • Underlines indicating combinatory reduction • Arrows indicating the direction of the application • X/Y Y : X/Y가 뒤의 Y와 결합하여 X가 된다. (순행 연산) • Y X/Y : X/Y가 앞의 Y와 결합하여 X가 된다. (역행 연산)

10. 2 the grammar • A dependency is defined as a 4-tuple: <hf, f, s, ha> • hf is the head word of the functor • f is the functor category • s is the argument slot • ha is the head word of the argument • Ex) the object dependency yielded by the first step of (3)

11. 2 the grammar • Variables • Be used to denote heads • Be used via unifications to pass head information from one category to another • Ex) the expanded category for the control verb persuade • Ex) I persuaded him to go to the party • The head of the infinitival complement’s subject • Is identified with the head of the object • Unification “passes” the head of the object to the subject of the infinitival

12. 2 the grammar • Raising • A syntactic process by which a NP or other element is moved from a SC into the structure of the larger clause • Ex) I believe [him to be honest] → I believe him [to be honest] • The kinds of lexical items that use the head passing mechanism • Are raising, auxiliary and control verbs, modifiers, and relative pronouns • The relative pronoun category • Show how heads are co-indexed for object-extraction

13. 2 the grammar • Type-rasing (T) and functional composition (B), along with co-indexing of heads • Mediate transmission of the head of the NP the company onto the object of buy

14. 2 the grammar • With the convention that arcs point away from arguments • The relevant argument slot in the functor category labels the arcs • Encode the subject argument of the to category as a dependency relation (Marks is a “subject” of to) • To encode every argument as a dependency

15. 3 The probability model

16. 3.1 estimating the dependency probabilities • W is the set of words in the data • C is the set of lexical categories

17. 3.1 estimating the dependency probabilities

18. 4 The parser • Parser는 2가지 단계에서 문장을 분석 • 1. 문장의 각 단어에 category 할당 • supertagger(Clark, 2002) : category 확률이 constant factor β안에 있는 모든 category 할당 • 문장에 대해 여러가지 category sequence가 가능 • Parser에 의해 return될 category sequence는 확률 모델에 의해 결정됨 • Supertagger의 2가지 역할 • 1. parser의 search space 줄여줌 • 2. Category sequence model 제공

19. 4 The parser • Supertagger는 “category dictionary”참조 • data에서 관찰된 category 집합을 각 단어마다 가짐 • 2. CKY bottom-up chart-parsing algorithm 적용 • Parser가 사용한 결합 규칙, 대등 규칙 • Type-raising, generalised forward composition…… • Type-raising : category NP, PP, S[adj]＼NP(형용사구)에 적용됨 • NP, PP, S[adj]＼NP가 발견되면 chart에 미리 정의된 type-raised category 추가 • type-raised category 집합은 CCGbank section 02-21의 빈번하게 발생한 type-raising rule에 기반함 • 8 type-raised categories for NP • 2 categories each for PP and S[adj]＼NP

20. 4 The parser • Parser는 또한 lexical rule 사용 • CCGbank section 02-21에서 200번 이상 발생한 것들 • Ex) ing형태의 동사구로부터 명사를 수식하는 어구 만들기 • Comma에 관한 규칙도 사용 • Ex) comma를 conjunct로 다룸 • John likes apples, bananas and pears • NP object가 3가지 head를 가짐, 모두 like의 직접 목적어 • Parser의 search space와 통계 모델 • If there is not already a constituent with the same head word, same category, and some DS with a higher of equal score • If the score for its dependency structure is within some factor α • A constituent is only placed in a chart cell

21. 5 experiments • 말뭉치 • Training : section 02-21 of the CCGbank (39,161) • Development : section 00 (1,901) • Testing : section 23 (2,379) • Category set : section 02-21 (10번 이상, 398) • Estimating the probabilities • P(C|S)의 estimate : CCGbank로부터 직접 획득 • To obtain dependencies for estimating P(D|C,S) • Tree에 대해 derivation동안 적용된 결합 규칙들을 찾고 dependency를 출력 • Increased the coverage on sec23 to 99%(2,352) • By identifying the cause of the parse failures and adding the additional rules and categories

22. 5 experiments • Initial parser • β=0.01 for the supertagger (an average of 3.8 c/w) • K=20 for the category dictionary • α=0.001 for the parser • 2,098 of the 2,352 sentences • received analysis, with 206 timing out and 48 failing • If any sentence took longer than 2 CPU minutes to parse • 48 no-analysis case : K=100 증가, 23문장 분석 • 206 time-out case : β=0.05 증가, 181문장 분석 • With 18 failing to parse, and 7 timing out • Almost 98% of the 2,352 unseen sentences

23. 6 results • To measure the performance of the parser • Compared the dependencies output by the parser with those in the gold standard • The category set distinguishes around 400 distinct types • Ex) tensed transitive buy is treated as a distinct category from infinitival transitive buy • More stringent (Penn Treebank 약 50개 품사 태그) • “distance measure” (Δ) : less useful • The CCG grammar provides many of the constraints given by Δ, and d.m. are biased against long-range dependencies

24. 6 results

25. 6 results • 다른 parser와의 비교 어렵다 • Different data or different sets of dependencies • The 24 cases of extracted objects in the gold-standard • that were passed down the object relative pronoun category • 10 (41.7%) were recovered correctly by the parser • 10 were incorrect because the wrong category was assigned to the relative pronoun • Reflect the fact that complementiser that is fifteen times as frequent as object relative pronoun that • The suppertagger alone gets 74% of the o.r.p. correct • Dependency model is further biased against object extractions • A first attempt at recovering these long-range dependencies

26. 6 results

27. 7 Conclusion and further work • Accurate, efficient wide-coverage parsing is possible with CCG • The parser is able to capture a number of long-range dependencies • Is necessary for any parser that aims to support wide-coverage semantic analysis • Long-range dependency recovery가 후처리 단계가 아니라 문법,파서와 통합된 과정 • Building alternative structures that include the long-range dependencies • using better motivated probability models