tailieunhanh - Báo cáo khoa học: "Entailment above the word level in distributional semantics"

We introduce two ways to detect entailment using distributional semantic representations of phrases. Our first experiment shows that the entailment relation between adjective-noun constructions and their head nouns (big cat |= cat), once represented as semantic vector pairs, generalizes to lexical entailment among nouns (dog |= animal). Our second experiment shows that a classifier fed semantic vector pairs can similarly generalize the entailment relation among quantifier phrases (many dogs|=some dogs) to entailment involving unseen quantifiers (all cats|=several cats). . | Entailment above the word level in distributional semantics Marco Baroni Ngoc-Quynh Do Chung-chieh Shan Raffaella Bernardi Free University of Bozen-Bolzano Cornell University University of Trento University of Tsukuba ccshan@ Abstract We introduce two ways to detect entailment using distributional semantic representations of phrases. Our first experiment shows that the entailment relation between adjective-noun constructions and their head nouns big cat 1 cat once represented as semantic vector pairs generalizes to lexical entailment among nouns dog animal . Our second experiment shows that a classifier fed semantic vector pairs can similarly generalize the entailment relation among quantifier phrases many dogs some dogs to entailment involving unseen quantifiers all cats several cats . Moreover nominal and quantifier phrase entailment appears to be cued by different distributional correlates as predicted by the type-based view of entailment in formal semantics. 1 Introduction Distributional semantics DS approximates linguistic meaning with vectors summarizing the contexts where expressions occur. The success of DS in lexical semantics has validated the hypothesis that semantically similar expressions occur in similar contexts Landauer and Dumais 1997 Lund and Burgess 1996 Sahlgren 2006 Schutze 1997 Turney and Pantel 2010 . Formal semantics FS represents linguistic meanings as symbolic formulas and assemble them via composition rules. FS has successfully modeled quantification and captured inferential relations between phrases and between sentences Montague 1970 Thomason 1974 Heim and Kratzer 1998 . The strengths of DS and FS have been complementary to date On one hand DS has induced large-scale semantic representations from corpora but it has been largely limited to the lexical domain. On the other hand FS has provided sophisticated models of sentence meaning but it has been largely limited to hand-coded

• Báo cáo khoa học
• Entailment above the word level
• distributional semantics
• Marco Baroni Ngoc Quynh Do
• scientific reports
• model language
• process natural language
• Dialog Control
• Natural Language System
• Dialog Control in a Natural Language System
• Expanding the Horizons
• Natural Language Interfaces
• Phil Hayes
• PROSPECTS OR PRACTICAL
• NATURAL LANGUAGE SYSTEMS
• Workshops
• NATURAL LANGUAGE
• INTERACTION WITH MACHINES
• Long Papers
• COMPUTER INTEBFACE DESIGN MURRAY TUROFF DEPARTMENT OF COMPUTER
• IiVFORMATION SCIENCE IIEW JERSEY INSTITUTE OF TECHNOLOGY
• EVALUATION OF NATURAL LANGUAGE
• INTERFACES TO DATABASE SYSTEMS
• A PANEL DISCUSSION
• database query system
• J
• Norwood Crout
• INTERFACES TO DATA BASE SYSTEMS
• Bozena Henisz
• INTERPRETING NATURAL LANGUAGE
• DATABASE UPDATES
• S
• Jermld Kaplan Jim David
• TRANSPORTABLE NATURAL LANGUAGE
• INTERFACES TO DATABASES
• Gary G
• Hendrlx and William H
• Lewis
• DATABASES THEORETICAL
• TECHNICAL ISSUES
• TRANSPORTABLE NATURAL LANGUAGE INTERFACES
• PROBLEMS AND TECHNIQUES
• Barbara J
• Grosz
• THEORETICAL TECHNICAL ISSUES
• NATURAL LANGUAGE ACCESS TO DATABASES
• R
• Petrick
• Domain Independent
• Database Access Systems
• ISSUES IN NATURAL LANGUAGE
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• A LOGIC PROGRAMMING PERSPECTIVE
• Sharon C
• Salveter David Maier
• PLANNING NATURAL LANGUAGE
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• Douglas E
• Appelt
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• NATURAL LANGUAGE GENERATION
• Kathleen R
• M
• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
• THE KEY TO THE SELECTION PROBLEM
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• Jeffrey Conklin David D
• McDonald
• A KNOWLEDGE ENGINEERING
• NATURAL LANGUAGE UNDERSTANDING
• Stuart C
• Shapiro
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• Mark H
• Bursteln