74.406 Natural Language Processing - Feature Structures - Feature Structures and Unification.

Slides:



Advertisements
Similar presentations
 Christel Kemke 2007/08 COMP 4060 Natural Language Processing Feature Structures and Unification.
Advertisements

Feature Structures and Parsing Unification Grammars Algorithms for NLP 18 November 2014.
CSA4050: Advanced Topics in NLP Semantics IV Partial Execution Proper Noun Adjective.
BİL711 Natural Language Processing1 Problems with CFGs We know that CFGs cannot handle certain things which are available in natural languages. In particular,
Chapter 4 Syntax.
Natural Language Processing - Parsing 1 - Language, Syntax, Parsing Problems in Parsing Ambiguity, Attachment / Binding Bottom vs. Top Down Parsing.
Grammars, constituency and order A grammar describes the legal strings of a language in terms of constituency and order. For example, a grammar for a fragment.
Dr. Abdullah S. Al-Dobaian1 Ch. 2: Phrase Structure Syntactic Structure (basic concepts) Syntactic Structure (basic concepts)  A tree diagram marks constituents.
Statistical NLP: Lecture 3
1 Features and Augmented Grammars Allen ’ s Chapter 4 J&M ’ s Chapter 11.
Artificial Intelligence 2004 Natural Language Processing - Syntax and Parsing - Language, Syntax, Parsing Problems in Parsing Ambiguity, Attachment.
 Christel Kemke 2007/08 COMP 4060 Natural Language Processing 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.
Features & Unification Ling 571 Deep Processing Techniques for NLP January 26, 2011.
Features & Unification Ling 571 Deep Processing Techniques for NLP January 31, 2011.
Syllabus Text Books Classes Reading Material Assignments Grades Links Forum Text Books עיבוד שפות טבעיות - שיעור עשר Chart Parsing (cont) Features.
Amirkabir University of Technology Computer Engineering Faculty AILAB Efficient Parsing Ahmad Abdollahzadeh Barfouroush Aban 1381 Natural Language Processing.
Artificial Intelligence 2005/06 From Syntax to Semantics.
 Christel Kemke 2007/08 COMP 4060 Natural Language Processing Feature Structures and Unification.
Features and Unification
1 Introduction to Computational Linguistics Eleni Miltsakaki AUTH Fall 2005-Lecture 2.
 Christel Kemke 2007/08 COMP 4060 Natural Language Processing Grammar Sentence Constructs.
1 CONTEXT-FREE GRAMMARS. NLE 2 Syntactic analysis (Parsing) S NPVP ATNNSVBD NP AT NNthechildrenate thecake.
Artificial Intelligence 2004 Natural Language Processing - Syntax and Parsing - Language Syntax Parsing.
Lect. 11Phrase structure rules Learning objectives: To define phrase structure rules To learn the forms of phrase structure rules To compose new sentences.
CS 4705 Lecture 11 Feature Structures and Unification Parsing.
Models of Generative Grammar Smriti Singh. Generative Grammar  A Generative Grammar is a set of formal rules that can generate an infinite set of sentences.
1 Basic Parsing with Context Free Grammars Chapter 13 September/October 2012 Lecture 6.
1 Features and Unification Chapter 15 October 2012 Lecture #10.
Chapter 16: Features and Unification Heshaam Faili University of Tehran.
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.
1 Natural Language Processing Lecture Notes 11 Chapter 15 (part 1)
NLP. Introduction to NLP Is language more than just a “bag of words”? Grammatical rules apply to categories and groups of words, not individual words.
Today Phrase structure rules, trees Constituents Recursion Conjunction
Chapter 4: Syntax Part V.
11 Chapter 14 Part 1 Statistical Parsing Based on slides by Ray Mooney.
2007CLINT-LIN-FEATSTR1 Computational Linguistics for Linguists Feature Structures.
Linguistic Essentials
Culture , Language and Communication
Artificial Intelligence: Natural Language
The man bites the dog man bites the dog bites the dog the dog dog Parse Tree NP A N the man bites the dog V N NP S VP A 1. Sentence  noun-phrase verb-phrase.
CSA2050 Introduction to Computational Linguistics Parsing I.
Natural Language - General
NLP. Introduction to NLP Motivation –A lot of the work is repeated –Caching intermediate results improves the complexity Dynamic programming –Building.
Section 11.3 Features structures in the Grammar ─ Jin Wang.
Making it stick together…
NLP. Introduction to NLP Shallow parsing Useful for information extraction –noun phrases, verb phrases, locations, etc. Example –FASTUS (Appelt and Israel,
Artificial Intelligence 2004
CS 4705 Lecture 7 Parsing with Context-Free Grammars.
1 Introduction to Computational Linguistics Eleni Miltsakaki AUTH Spring 2006-Lecture 2.
SYNTAX.
1 Natural Language Processing Lecture 6 Features and Augmented Grammars Reading: James Allen NLU (Chapter 4)
◦ Process of describing the structure of phrases and sentences Chapter 8 - Phrases and sentences: grammar1.
NATURAL LANGUAGE PROCESSING
Chapter 11: Parsing with Unification Grammars Heshaam Faili University of Tehran.
Natural Language Processing Vasile Rus
Beginning Syntax Linda Thomas
Statistical NLP: Lecture 3
Basic Parsing with Context Free Grammars Chapter 13
CKY Parser 0Book 1 the 2 flight 3 through 4 Houston5 6/19/2018
Syntax.
CKY Parser 0Book 1 the 2 flight 3 through 4 Houston5 11/16/2018
BBI 3212 ENGLISH SYNTAX AND MORPHOLOGY
Natural Language - General
Introduction to Linguistics
Linguistic Essentials
CPSC 503 Computational Linguistics
Principles and Parameters (I)
Artificial Intelligence 2004 Speech & Natural Language Processing
Presentation transcript:

Natural Language Processing - Feature Structures - Feature Structures and Unification

Feature Structures, Grammar, Parsing Feature Structures describe additional syntactic-semantic information, like category, person, number, e.g.goes  specify feature structure constraints (agreements) as part of the grammar rules during parsing, check agreements of feature structures (unification) example S  NP VP =

Feature Structures, Grammar, Parsing Feature Structures describe additional syntactic-semantic information, like category, person, number, e.g.goes  specify feature structure constraints (agreements) as part of the grammar rules during parsing, check agreements of feature structures (unification) example S  NP VP =

Feature Structures Feature structures describe linguistic attributes or features like number, person associated with words or syntactic constituents like noun phrase. Feature structures are sets of features and values, e.g. hat[Numbersing ] buys[Person 3 ] [Numbersing ]

Feature Structures - Agreement Feature structures can be collected in one ‘variable’ called agreement. buys agreement [Person 3] [Number sing] Often these feature structures are referenced through an identifier.

Feature Structures as Constraints Ungrammatical expressions like He go...or We goes... One scarf... He sleeps the book... can be excluded using feature constraints. Feature structures are used to describe grammatical attributes of the respective item (word, syntactic category, phrase) like number, person,... and connections between them (constraints), which are checked during processing (unification). example S  NP VP = =

Add to feature structure the syntactic category cat: buys cat verb agreement [Person 3 ] [Number sing] Feature Structures and Categories

Compare and combine feature structures: he buys buys cat verb agreement [Person 3 ] [Number sing] he cat noun agreement [Person 3 ] [Number sing] Feature Structures and Unification 1

S → NP VP = = buys cat verb agreement [Person 3 ] [Number sing] he cat noun agreement [Person 3 ] [Number sing] Using Feature Structures must match in unification

Unification of Feature Structures Agreement is checked by the unification operation and merged feature structure is determined according to the following cases: [feature i value i ] |_| [feature i value i ] = [feature i value i ] [feature i value i ] |_| [feature i value k ] = fail if value i  value k [feature i value i ] |_| [feature i undef.] = [feature i value i ] [feature i value i ] |_| [feature k value k ] = feature i value i feature k value k if feature i  feature k

"Inheritance" of Feature Structures Feature structures are "inherited" during parsing or generation, using the feature structure of the head of a phrase: NP  det Nom NP i=1,..,n  det Nom i=1,..,n Nom i=1,..,n  pre-Nom Nom i=1,..,n post-Nom Complex feature structures are often referenced through identifying numbers. Constraints on feature structures can be checked using these references; and the same feature structure can be used in different parts of the parse tree through reference. head

Features and Subcategorization NP modifiers or Verb complements head noun + modifiers + agreement head verb + complements + agreements “... the man who chased the cat out of the house...” “... the man chased the barking dog who bit him...” Agreements are passed on / inherited within phrases, e.g. agreement of VP derived from Head-Verb of VP: determined by

References D. Jurafsky & J. H. Martin, Speech and Language Processing, Prentice-Hall, (Chapters 9 and 10) J. Eisner, Intro to NLP, Department of Computer Science, Johns-Hopkins University