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Wide-Coverage CCG Parsing with Quantifier Scope
 

Wide-Coverage CCG Parsing with Quantifier Scope

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    Wide-Coverage CCG Parsing with Quantifier Scope Wide-Coverage CCG Parsing with Quantifier Scope Presentation Transcript

    • Introduction Methodology ResultsWide-Coverage CCG Parsing with Quantifier Scope Dimitrios Kartsaklis MSc Thesis, University of Edinburgh Supervisor: Professor Mark Steedman July 18, 2011 Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 1/ 28
    • Introduction Methodology ResultsIntroduction A Natural Language Processing project Dealing with semantics, and specifically with quantifier scope ambiguities Purpose: The creation of a wide-coverage semantic parser capable of handling quantifier scope ambiguities using Generalized Skolem Terms Grammar formalism: Combinatory Categorial Grammar (CCG) Logical form: First-order logic using λ-calculus as “glue” language Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 2/ 28
    • Introduction Methodology ResultsQuantification All known human languages make use of quantification. In English: • Universal quantifiers (∀): every, each, all, ... • Existential quantifiers (∃): a, some, ... • Generalized quantifiers: most, at least, few, ... Traditional representations using first-order logic and λ-calculus: • Universal: λp.λq.∀x[p(x) → q(x)] • Existential: λp.λq.∃x[p(x) ∧ q(x)] Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 3/ 28
    • Introduction Methodology ResultsCompositionality Frege’s principle: “The meaning of the whole is a function of the meaning of its parts” Example: “Every boy likes some girl” Every boy likes some girl NP/N N (SNP)/NP NP/N N : λp.λq.∀y [p(y ) → q(y )] : λy .boy (y ) : λx.λy .likes(y , x) : λp.λq.∃x[p(x) ∧ q(x)] : λx.girl(x) > > NP NP : λq.∀y [boy (y ) → q(y )] : λq.∃x[girl(x) ∧ q(x)] > SNP : λy .∃x[girl(x) ∧ likes(y , x)] < S : ∀y [boy (y ) → ∃x[girl(x) ∧ likes(y , x)]] Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 4/ 28
    • Introduction Methodology ResultsQuantifier scope ambiguities Example: “Every boy likes some girl” • ∀x[boy (x) → ∃y [girl(y ) ∧ likes(x, y )]] (every boy likes a possibly different girl) However: Not the only meaning: • ∃y [girl(y ) ∧ ∀x[boy (x) → likes(x, y )]] (there is a specific girl who is liked by every boy ) But our semantics is surface-compositional, so only the first reading is allowed by syntax We need a quantification method to deliver both readings in a single syntactic derivation Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 5/ 28
    • Introduction Methodology ResultsUnderspecification A solution to the problem: provide underspecified representations of the quantified expressions without explicitly specify their scope: • loves(x1 , x2 ), (λq.∀x[boy (x) → q(x)], 1), (λq.∃y [girl(y ) ∧ q(y )], 2) Specification is performed in a separate step, after the end of the syntactic derivation, by combining the available quantified expressions in every possible way The most known underspecification technique is Cooper storage Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 6/ 28
    • Introduction Methodology ResultsUnderspecification problems Decoupling the semantic derivation from the syntactic combinatorics can lead to problems: • Possibly equivalent logical forms:“Some boy likes some girl” a. ∃x[boy (x) ∧ ∃y [girl(y ) ∧ likes(x, y )]] b. ∃y [girl(y ) ∧ ∃x[boy (x) ∧ likes(x, y )]] • Scope asymmetries: “Every boy likes, and every girl detests, some saxophonist”: ∀x[boy (x) → ∃y [sax(y ) ∧ likes(x, y )]]∧ ∃v [sax(v ) ∧ ∀z[girl(z) → detests(z, v )]] • Intermediate readings: “Some teacher showed every pupil every movie”: ∀x[movie(x) → ∃y [teacher (y )∧ ∀z[pupil(x) → showed(x, y , z)]]] Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 7/ 28
    • Introduction Methodology ResultsSkolemization If existentials cause such problems, why not remove them altogether? Skolemization: The process of replacing an existential quantifier with a function of all universally quantified variables in whose scope the existential falls. Example 1: ∀x∃y ∀z.P(x, y , x) =⇒ ∀x∀z.P(x, sk(x), z) • The existential of y is replaced by sk(x), since x was the only preceding universal. Example 2: ∃y ∀x∀z.P(x, y , x) =⇒ ∀x∀z.P(x, sk(), z) • Now sk() is a function without arguments – a constant. Example 3: “Every boy likes some girl”: • Normal form: ∀x[boy (x) → ∃y [girl(y ) ∧ likes(x, y )]] {x} • Skolemized form: ∀x[boy (x) → likes(x, skgirl )] Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 8/ 28
    • Introduction Methodology ResultsOne step further: Generalized Skolem Terms The only true quantifiers in English are the universals every, each, and their relatives Every other non-universal quantifier should be associated with a (yet unspecified) Skolem term Skolem terms can be specified according to their environment at any step of the derivation process into a generalized form E skn:p;c where E is the environment (preceding universals) and • n the number of the originating noun phrase • p a nominal property (e.g. “girl”) • c a cardinality condition (e.g. λs.|s| > 1) (Mark Steedman, Natural Semantics of Scope, currently in publication by MIT Press) Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 9/ 28
    • Introduction Methodology ResultsTwo available readings Specification takes places in the beginning of the derivation Every boy likes some girl NP/N N (SNP)/NP NP/N N : λp.λq.∀y [p(y ) → q(y )] : λy .boy (y ) : λx.λy .likes(y , x) : λp.λq.q(skolem(p)) : λx.girl(x) > > NP : λq.∀y [boy (y ) → q(y )] NP : λq.q(skolem(λx.girl(x))) ................................... {} NP : λq.q(skgirl ) > {} SNP : λy .likes(y , skgirl ) < {} S : ∀y [boy (y ) → likes(y , skgirl )] Specification takes places at the end of the derivation Every boy likes some girl NP/N N (SNP)/NP NP/N N : λp.λq.∀y [p(y ) → q(y )] : λy .boy (y ) : λx.λy .likes(y , x) : λp.λq.q(skolem(p)) : λx.girl(x) > > NP : λq.∀y [boy (y ) → q(y )] NP : λq.q(skolem(λx.girl(x))) > SNP : λy .likes(y , skolem(λx.girl(x))) < S : ∀y [boy (y ) → likes(y , skolem(λx.girl(x)))] .................................................................................................... {y } S : ∀y [boy (y ) → likes(y , skgirl )] Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 10/ 28
    • Introduction Methodology ResultsAdvantages Provides a global solution to the most important quantification problems Skolem terms are part of the semantic theory, not ad-hoc mechanisms Easy integration with CCG parsers – no significant increase in computational complexity Semantic derivation is performed “on-line”, based on the combinatory rules of CCG • This limits the degree of freedom in which the available readings are derived, so non-attested or redundant readings are excluded Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 11/ 28
    • Introduction Methodology ResultsA proof of concept Purpose of the project: The creation of a wide-coverage semantic parser that applies the previously described quantification approach. Main tasks: 1. Create a wide-coverage probabilistic syntactic parser 2. Create a λ-calculus framework for the logical forms 3. Integrate the semantic combinatorics to the parser 4. Provide appropriate logical forms for the CCG lexicon Eventually: Provide a proof of concept for the theory by testing the results in specific quantification cases Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 12/ 28
    • Introduction Methodology ResultsSyntactic parsing Parser is based on the OpenCCG framework • Well-tested API for parsing, supports every aspect of CCG Two additions: • A supertagger for assigning initial categories to the words (Clark & Curran) • A probabilistic model incorporating head-word dependencies (Hockenmaier) Standard interpolation techniques for dealing with sparse data problems Beam search for pruning the search space Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 13/ 28
    • Introduction Methodology ResultsProbabilistic model A generative model on local trees level (trees of depth 1) (Hockenmaier) Baseline version: The probability of a local tree with root N and children H and S is the product of: • An expansion probability P(expansion|N) • A head probability P(H|N, expansion) • A non-head probability P(S|N, expansion, H) • A lexical probability P(w |N, expansion = leaf ) The overall probability of a derivation is the product of the probabilities of all local trees Head-word dependencies version: Also take in account the relationships between the heads of local trees Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 14/ 28
    • Introduction Methodology ResultsSemantic forms An object-oriented approach Formulas are represented as a set of nested objects • Example: ‘‘Every man walks” Infix notation: ∀x[man(x) → walks(x)] Prefix notation: all(x,imp(man(x),walks(x))) all(x, expr) imp(expr1, expr2) xx man(x) walk(x) xx xx Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 15/ 28
    • Introduction Methodology ResultsObject hierarchy Expression LambdaAbstraction FunctionalApplication FirstOrderExpression Variable SkolemTerm GenericPredicate Quantification Conjunction ... GeneralizedST Constant BindingTerm PlainVariable Lambda Quantified Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 16/ 28
    • Introduction Methodology ResultsSkolem Term representations A single linked packed structure: Object references (pointers) SkolemTerm to specifications GeneralizedSkolemTerm GeneralizedSkolemTerm GeneralizedSkolemTerm (Specification1) (Specification2) (Specification3) Example: “A boy ate a pizza” skolem skolem ate( boy , pizza ) sk sk Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 17/ 28
    • Introduction Methodology Resultsβ-conversion β-conversion: The process of substituting a bound variable in the body of a λ-abstraction by the argument passed to the function A stack-based method (Blackburn & Bos) 1. When the expression is an application, push its argument to the stack and discard the outermost application object. 2. If the expression is a λ-abstraction, throw away the λ-term, pop the item at the top of the stack, and substitute it for every occurrence of the correlated variable. 3. If the expression is neither an application nor a λ-abstraction, β-convert its sub-expressions. Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 18/ 28
    • Introduction Methodology Resultsβ-conversion Example: “John loves Mary” John loves Mary NP : john (SNP)/NP : λx.λy .loves(y , x) NP : mary > SNP : λy .loves(y , mary ) < S : loves(john, mary ) Expression Stack 1 app(app(lam(x,lam(y,loves(y,x))),mary),john) [] 2 app(lam(x,lam(y,loves(y,x))),mary) [john] 3 lam(x,lam(y,loves(y,x))) [mary,john] 4 lam(y,loves(y,mary)) [john] 5 loves(john,mary) [] Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 19/ 28
    • Introduction Methodology ResultsOpenCCG and CKY OpenCCG uses the CKY algorithm: NP S S/(SNP) S/NP S Mary (SNP)/NP SNP married NP S/(SNP) John Mary married John Mary married John NP (SNP)/NP NP NP (SNP)/NP NP >T > S/(SNP) SNP >B < S/NP S > S Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 20/ 28
    • Introduction Methodology ResultsCKY modifications An additional step is introduced in the inner loop of the CKY algorithm, called skolem term specification: 1. For each skolem term ST in the logical form Λ, collect the new environment (preceding universals) of ST . 2. If the new environment is different than the old environment, specify a new Generalized Skolem Term and add it to the specifications list of ST . where ΛA is the logical form of a result category A that has been produced by the application of some CCG rule Environment is always readily available thanks to the nested internal structure Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 21/ 28
    • Introduction Methodology ResultsSyntax-to-semantics mapping The syntactic rules are mapped to semantic transformations based on the following table: Rule λ-abstraction fapp(ΛB , ΛC ) ΛA = app(ΛB , ΛC ) bapp(ΛB , ΛC ) ΛA = app(ΛC , ΛB ) fcomp(ΛB , ΛC ) ΛA = λ¯.app(ΛB , app(¯, ΛC )) x x bcomp(ΛB , ΛC ) ΛA = λ¯.app(ΛC , app(ΛB , x )) x ¯ λ¯ is a vector containing the outer λ-terms of the predicate x that remain to be filled after the composition Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 22/ 28
    • Introduction Methodology ResultsThe semantic lexicon A simple form that allows various degrees of grouping between categories and words Each entry is comprised by a descriptive title, a list of CCG categories, a list of surface forms, and a logical expression in prefix notation Example: The entry for universal quantifiers [universal] categories: (S/(SNP))/N|NP/N words: every|each|all LF: lam(p,lam(q,all(x,impl(app(p,x),app(q,x))))) Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 23/ 28
    • Introduction Methodology ResultsSyntactic parsing results Probabilistic model trained on Sections 02-21 of CCGbank Evaluation has been performed on Section 23 of CCGbank Parser Cov. LexCat P, H, S Clark et al. (2002) 95.0 90.3 81.8 90.0 Hockenmaier (2003) 99.8 92.2 85.1 91.4 Clark & Curran (2004) 99.6 93.6 86.4 92.3 96.6 92.4 71.8 78.8 Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 24/ 28
    • Introduction Methodology ResultsEvaluation of semantic component The semantic aspect of the parser was tested on 50 sentences presenting a wide range of linguistic challenges More specifically, the following cases were tested: • Scope inversion • “Donkey” sentences • Scope asymmetries • Intermediate scope • Spurius readings • Coordination cases • Generalized quantifiers In almost every case the results conformed to the predictions of the theory Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 25/ 28
    • Introduction Methodology ResultsForm of the derivations Sample derivation: “Some logician proved every theorem” {}{x} • ∀x[theorem(x) → proved(sklogician , x)] (lex) Some :- NP/N : lam:p.lam:q.q(skolem(p)) (lex) logician :- N : lam:x.logician(x) (>) Some logician :- NP : lam:q.q(sk{lam:x.logician(x)}_{}) (lex) proved :- (SNP)/NP : lam:x.lam:y.proved(y,x) (lex) every :- NP/N : lam:p.lam:q.all:x[p(x)->q(x)] (lex) theorem :- N : lam:x.theorem(x) (>) every theorem :- NP : lam:q.all:x[theorem(x)->q(x)] (>) proved every theorem :- SNP : lam:y.all:x[theorem(x)->proved(y,x)] (gram) type-changing3: SNP => NPNP (tchange3) proved every theorem :- NPNP : lam:y.all:x[theorem(x)->proved(y,x)] (<) Some logician proved every theorem :- NP : all:x[theorem(x)->proved(sk{lam:x.logician(x)}_{}_{x},x)] Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 26/ 28
    • Introduction Methodology ResultsHowever... Probabilistic model too weak to properly guide the semantic derivation in many cases Wide-coverage parsers stretch the flexibility of the grammar in order to provide some sort of analysis, even a wrong one Example: “Every man walks and talks” Every man walks and talks NP (SNP)/NP conj ∗N > N N ⇒ NP > SNP < S In such cases, proper semantic derivation is blocked – semantics simply cannot follow syntax Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 27/ 28
    • Introduction Methodology ResultsFuture work Fine-tuning of the probabilistic model Extending the semantic lexicon for really wide-coverage semantic parsing Adding semantic aspects such as negation and polarities Improve coverage of generalized quantifiers Presenting the results in a less cryptic form, by properly unpacking and enumerate all the available readings Dimitrios Kartsaklis Wide-Coverage CCG Parsing with Quantifier Scope 28/ 28