CSA4050: Advanced Topics in NLP

Slides:



Advertisements
Similar presentations
Problems of syntax-semantics interface ESSLLI 02 Trento.
Advertisements

Computational Semantics Aljoscha Burchardt, Alexander Koller, Stephan Walter, Universität des Saarlandes,
October 2004CSA4050: Semantics III1 CSA4050: Advanced Topics in NLP Semantics III Quantified Sentences.
 Christel Kemke 2007/08 COMP 4060 Natural Language Processing Feature Structures and Unification.
Natural Language Processing Lecture 2: Semantics.
November 2008NLP1 Natural Language Processing Definite Clause Grammars.
CSA2050: DCG I1 CSA2050 Introduction to Computational Linguistics Lecture 8 Definite Clause Grammars.
First-Order Logic (and beyond)
07/05/2005CSA2050: DCG31 CSA2050 Introduction to Computational Linguistics Lecture DCG3 Handling Subcategorisation Handling Relative Clauses.
CSA4050: Advanced Topics in NLP Semantics IV Partial Execution Proper Noun Adjective.
Semantics (Representing Meaning)
Semantic Analysis Read J & M Chapter 15.. The Principle of Compositionality There’s an infinite number of possible sentences and an infinite number of.
CSA2050: DCG IV1 CSA2050: Definite Clause Grammars IV Handling Gaps II Semantic Issues.
April 2010CSA50061 Logic, Representation and Inference Simple Question Answering NL Access to Databases Semantics of Questions and Answers Simple Interpreters.
DEFINITE CLAUSE GRAMMARS Ivan Bratko University of Ljubljana Faculty of Computer and Information Sc.
Linguistic Theory Lecture 8 Meaning and Grammar. A brief history In classical and traditional grammar not much distinction was made between grammar and.
LTAG Semantics on the Derivation Tree Presented by Maria I. Tchalakova.
Natural Language Processing - Feature Structures - Feature Structures and Unification.
NLP and Speech Course Review. Morphological Analyzer Lexicon Part-of-Speech (POS) Tagging Grammar Rules Parser thethe – determiner Det NP → Det.
NLP and Speech 2004 Feature Structures Feature Structures and Unification.
LING 364: Introduction to Formal Semantics Lecture 10 February 14th.
LING 364: Introduction to Formal Semantics Lecture 9 February 9th.
Parsing: Features & ATN & Prolog By
LING 364: Introduction to Formal Semantics
Artificial Intelligence 2005/06 From Syntax to Semantics.
LING 364: Introduction to Formal Semantics Lecture 4 January 24th.
LING 364: Introduction to Formal Semantics Lecture 11 February 16th.
Natural Language Query Interface Mostafa Karkache & Bryce Wenninger.
LING 364: Introduction to Formal Semantics Lecture 13 February 23rd.
LING 364: Introduction to Formal Semantics Lecture 5 January 26th.
LING/C SC/PSYC 438/538 Lecture 19 Sandiway Fong 1.
February 2009Introduction to Semantics1 Logic, Representation and Inference Introduction to Semantics What is semantics for? Role of FOL Montague Approach.
October 2004csa4050: Semantics II1 CSA4050: Advanced Topics in NLP Semantics II The Lambda Calculus Semantic Representation Encoding in Prolog.
Logic Programming Lecture 6: Parsing, Difference Lists and Definite Clause Grammars.
LING 388: Language and Computers Sandiway Fong Lecture 17.
October 2004CSA4050 Advanced Techniques in NLP 1 CSA4050: Advanced Topics in NLP Semantics 6 Semantics of Questions and Assertions involving Quantification.
IV. SYNTAX. 1.1 What is syntax? Syntax is the study of how sentences are structured, or in other words, it tries to state what words can be combined with.
November 2003CSA4050: Semantics I1 CSA4050: Advanced Topics in NLP Semantics I What is semantics for? Role of FOL Montague Approach.
Semantic Analysis CMSC Natural Language Processing May 8, 2003.
October 2004CSA4050: Semantics III1 CSA4050: Advanced Topics in NLP Semantics III Quantified Sentences.
Computing Science, University of Aberdeen1 CS4025: Logic-Based Semantics l Compositionality in practice l Producing logic-based meaning representations.
Artificial Intelligence: Natural Language
Semantic Construction lecture 2. Semantic Construction Is there a systematic way of constructing semantic representation from a sentence of English? This.
Interpreting Language (with Logic)
Rules, Movement, Ambiguity
CSA2050 Introduction to Computational Linguistics Parsing I.
Computational Semantics Day II: A Modular Architecture
Building a Semantic Parser Overnight
November 2006Semantics I1 Natural Language Processing Semantics I What is semantics for? Role of FOL Montague Approach.
November 9, Lexicon (An Interacting Subsystem in UG) Part-I Rajat Kumar Mohanty IIT Bombay.
LING 388: Language and Computers Sandiway Fong Lecture 16.
CAS LX 502 9b. Formal semantics Pronouns and quantifiers.
April 2010Semantic Grammar1 A short guide to Blackburn’s Grammar of English.
Logic Programming Lecture 6: Parsing and Definite Clause Grammars.
October 2004csa4050: Semantics V1 CSA4050: Advanced Topics in NLP Semantics V NL Access to Databases Semantics of Questions and Answers Simple Interpreters.
MENTAL GRAMMAR Language and mind. First half of 20 th cent. – What the main goal of linguistics should be? Behaviorism – Bloomfield: goal of linguistics.
SYNTAX.
November 2008NL Semantics V1 Advanced Topics in NLP Semantics V NL Access to Databases Semantics of Questions and Answers Simple Interpreters for Questions.
Statistical NLP: Lecture 3
Semantics (Representing Meaning)
Natural Language Processing
LING/C SC/PSYC 438/538 Lecture 21 Sandiway Fong.
LING 581: Advanced Computational Linguistics
CSA4050: Advanced Topics in NLP
Predicates and Quantifiers
CSA2050 Introduction to Computational Linguistics
Natural Language Processing
Principles and Parameters (I)
Semantics 2: Syntax-Semantics Interface
Presentation transcript:

CSA4050: Advanced Topics in NLP Semantics III Quantified Sentences November 2008 HLT Semantics III

Outline Language Sentences Determiners Noun Phrases Syntactic Structure Logic Generalised Quantifiers Higher order functions Translation into Prolog Syntax-Semantics Interface November 2008 HLT Semantics III

Determiners and Quantifiers in Language and Logic A dog barked x dog(x) & bark(x) Every dog barked x dog(x)  bark(x) Fido chased a cat x cat(x) & chase(fido,x) Every dog chased a cat x dog(x)  (y cat(x) & chase(x,y))) November 2008 HLT Semantics III

Syntactic Shape vs. Semantic Shape John walks semantics: walk(suzie). Every man talks semantics: all(X, man(X)  talk(X)) S NP VP Suzie walks Det N talks Every man November 2008 HLT Semantics III

Problem Similar syntactic shape Dissimilar semantic shape How is this possible if the syntax drives the combination of semantic fragments as per rule-to-rule hypothesis? Answer: be creative about logical forms and semantic combination rules November 2008 HLT Semantics III

Montague Solution Reorganising the semantic combination rules operating between VP and NP in rules such as s(S) --> np(NP), vp(VP). We will be considering [NP]([VP]) versus [VP]([NP]). NPs as higher order functions Analyse LF of quantified sentences November 2008 HLT Semantics III

LF of Quantified Sentences LF of quantified sentences has a general shape involving a restrictor predicate R a scope predicate S R restricts the set of things we are talking about S says something further about set element(s) a logical quantifier Q a bound variable V a logical operator O connecting R and S November 2008 HLT Semantics III

x lecturer(x)  lazy(x) Examples All lecturers are lazy x lecturer(x)  lazy(x) Restrictor = lecturers Scope = lazy Quantifier = All Operator = implies Bound Variable = x November 2008 HLT Semantics III

Examples There is a lazy lecturer x lecturer(x) & lazy(x) Restrictor = lecturers Scope = lazy Quantifier = exist Operator = and Bound Variable = x November 2008 HLT Semantics III

Anatomy of Quantified Sentences Logic Q V R O S x m(x)  w(x)  x m(x)  w(x) x d(x) & b(x)  d(x) & b(x) x d(x)  (h(x) & b(x)) h(x) & b(x) November 2008 HLT Semantics III

Generalized Quantifiers We adopt the following generalized quantifier representation for LF in which quantifier is a 3-place predicate: Q(<variable>,<restrictor>,<scope>) Operator is omitted. Examples all(X,man(X),walk(X)) exist(X,man(X),walk(X)) the(X,man(X),climbed(X,everest)) most(X,lecturer(X),poor(X)) November 2008 HLT Semantics III

NP as higher order function Q^all(X,man(X),Q) every man VP Y^walk(Y) walks S all(X,man(X),walk(X)) November 2008 HLT Semantics III

Encoding in Prolog The VP remains as before, ie X^walks(X) The quantified NP every man will be of the form Q^all(X,man(X),Q) The semantic rule for S now ensures that the NP function is applied to the VP function. s(S)--> np(NP),vp(VP), {reduce(NP,VP,S)} November 2008 HLT Semantics III

DCG with Quantification Program 1 % grammar s(S) --> np(NP), vp(VP), {reduce(NP,VP,S)} vp(VP) --> v(V). % lexicon v(X^walk(X)) --> [walks]. np(Q^all(X,man(X),Q)) --> [every,man]. November 2008 HLT Semantics III

Result ?- s(X,[every,man,walks],[]). X = all(_G397, man(_G397), _G405^walk(_G405)) all(x, man(x), y^walk(y)) What is wrong with this? How can we fix it? We need to force the variables to be identical using reduce November 2008 HLT Semantics III

Result ?- s(X,[every,man,walks],[]). X = all(_G397, man(_G397), _G405^walk(_G405)) all(x, man(x), y^walk(y)) What is wrong with this? The variables _G397 and _G405 are distinct. They should be identical. The consequent of the implication is a λ expression How can we fix it? We need to force the variables to be identical using reduce November 2008 HLT Semantics III

DCG with Quantification Program 2 % grammar s(S) --> np(NP), vp(VP), {reduce(NP,VP,S)} vp(VP) --> v(V). % lexicon v(X^walk(X)) --> [walks]. np(Q^all(X,man(X),P)) --> [every,man], {reduce(Q,X,P)}. November 2008 HLT Semantics III

Result The effect of the reduce clause is ?- s(X,[every,man,walks],[]). X = all(_G397, man(_G397),walk(_G397)) The effect of the reduce clause is to identify the appropriate variables to remove the λ variable November 2008 HLT Semantics III

Handling Quantified NPs Before we cheated by having every man as a lexical item. np(Q^all(X,man(X),P)) --> [every,man], { reduce(Q,X,P)}. Now we see what is involved in analysing the NP from its parts. Step 1 is to write a new syntactic rule np(NP) --> d(D), n(N). How does the semantics work? November 2008 HLT Semantics III

LF of determiners Key idea is determiner has LF of a 2-argument function corresponding to R and S which become bound during processing. λR.λS.Q(V,R,S) where Q is associated with the particular determiner When we apply this function to the adjacent noun, we obtain the LF of the NP. November 2008 HLT Semantics III

How NP is created D R^S^all(X,R,S) every N Y^man(Y) man NP S^all(X,man(X),S) November 2008 HLT Semantics III

Fitting the Semantics Together Handle the quantified NP np(NP) --> d(D), n(N), {reduce(D,N,NP)}. Add lexical entry for every d(RL^SL^all(X,R,S)) -->[every], {reduce(RL,X,R), reduce(SL,X,S) }. November 2008 HLT Semantics III

DCG with Quantification Program 3 % grammar s(S) --> np(NP), vp(VP), {reduce(NP,VP,S)}. np(NP) --> d(D), n(N), {reduce(D,N,NP) }. vp(VP) --> v(VP). % lexicon v(X^walk(X)) --> [walks]. n(X^man(X)) --> [man]. d(RL^SL^all(X,R,S) --> [every], {reduce(RL,X,R), reduce(SL,X,S) }. November 2008 HLT Semantics III

Trace >: (7) s(_G510, [every, man, walks], []) >: (8) np(_L183, [every, man, walks], _L184) >: (9) d(_L205, [every, man, walks], _L206) <: (9) d((X^R)^ (X^S)^all(X, R, S), [every, man, walks], [man, walks]) >: (9) n(_L207, [man, walks], _L208) <: (9) n(Z^man(Z), [man, walks], [walks]) >: (9) reduce((X^R)^ (X^S)^all(X, R, S), Z^man(Z), _L183) <: (9) reduce((X^man(X))^ (X^S)^all(X, man(X), S), X^man(X), (X^S)^all(X, man(X), S)) <: (8) np((X^S)^all(X, man(X), S), [every, man, walks], [walks]) >: (8) vp(_L185, [walks], _L186) >: (9) v(_L185, [walks], _L186) <: (9) v(Y^walk(Y), [walks], []) <: (8) vp(Y^walk(Y), [walks], []) >: (8) reduce((X^S)^all(X, man(X), S), Y^walk(Y), _G510) <: (8) reduce((X^walk(X))^all(X, man(X), walk(X)), X^walk(X), all(X, man(X), walk(X))) <: (7) s(all(X, man(X), walk(X)), [every, man, walks], []) November 2008 HLT Semantics III

Summary Quantification crops up a lot in NL To handle quantified sentences, there is a mismatch in shape between syntax and semantics Need to reorganise the semantic rules (NP applies to VP not vice versa). Representation of quantifier as a higher-order function. November 2008 HLT Semantics III