tailieunhanh - Báo cáo khoa học: "Inference Rules and their Application to Recognizing Textual Entailment"

In this paper, we explore ways of improving an inference rule collection and its application to the task of recognizing textual entailment. For this purpose, we start with an automatically acquired collection and we propose methods to refine it and obtain more rules using a hand-crafted lexical resource. Following this, we derive a dependency-based structure representation from texts, which aims to provide a proper base for the inference rule application. The evaluation of our approach on the recognizing textual entailment data shows promising results on precision and the error analysis suggests possible improvements. . | Inference Rules and their Application to Recognizing Textual Entailment Georgiana Dinu Saarland University Campus D-66123 Saarbriicken dinu@ Rui Wang Saarland University Campus D-66123 Saarbrucken rwang@ Abstract In this paper we explore ways of improving an inference rule collection and its application to the task of recognizing textual entailment. For this purpose we start with an automatically acquired collection and we propose methods to refine it and obtain more rules using a hand-crafted lexical resource. Following this we derive a dependency-based structure representation from texts which aims to provide a proper base for the inference rule application. The evaluation of our approach on the recognizing textual entailment data shows promising results on precision and the error analysis suggests possible improvements. 1 Introduction Textual inference plays an important role in many natural language processing NLP tasks. In recent years the recognizing textual entailment RTE Dagan et al. 2006 challenge which focuses on detecting semantic inference has attracted a lot of attention. Given a text T several sentences and a hypothesis H one sentence the goal is to detect if H can be inferred from T. Studies such as Clark et al. 2007 attest that lexical substitution . synonyms antonyms or simple syntactic variation account for the entailment only in a small number of pairs. Thus one essential issue is to identify more complex expressions which in appropriate contexts convey the same or similar meaning. However more generally we are also interested in pairs of expressions in which only a uni-directional inference relation holds1. 1We will use the term inference rule to stand for such concept the two expressions can be actual paraphrases if the relation is bi-directional A typical example is the following RTE pair in which accelerate to in H is used as an alternative formulation for reach speed of in T. T The high-speed train scheduledfor

• Báo cáo khoa học
• Inference Rules
• their Application to Recognizing
• Textual Entailment
• scientific reports
• model language
• process natural language
• USING PLAUSIBLE INFERENCE RULES
• DESCRIPTION PLANNING
• Alison Cawsey
• Database Systems
• Functional Dependencies
• Normalization for Relational DBs
• Inference Rules for FDs
• Equivalence of Sets of FDs
• Minimal Sets of FDs
• Representing Knowledge in Computer
• Representing knowledge
• Metrics for assessing knowledge
• Logic representation
• Semantics networks
• Parse engine
• Language engine
• Rules engine
• Inference engine
• ASCII characters
• Lecture Database management systems
• Bài giảng Hệ quản trị cơ sở dữ liệu
• Database management systems
• Functional dependency
• Unique value
• 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
• ACCESS TO DATABASES
• A LOGIC PROGRAMMING PERSPECTIVE
• Sharon C
• Salveter David Maier
• PLANNING NATURAL LANGUAGE
• EXPRESSIONS REFERRING
• Douglas E
• Appelt
• THE TEXT SYSTEM
• NATURAL LANGUAGE GENERATION
• Kathleen R
• M
• MENTING A DATABASE
• KNOWLEDGE REPRESENTATION
• THE KEY TO THE SELECTION PROBLEM
• E
• Jeffrey Conklin David D
• McDonald
• A KNOWLEDGE ENGINEERING
• NATURAL LANGUAGE UNDERSTANDING
• Stuart C
• Shapiro
• NATURAL LANGUAGE TEXTS
• NECESSARILY GRAMMATICAL
• EVEN COMPLETE
• UNGRAMHATICALITY
• EXTRA GRAMMATICALITY
• NATURAL LANGUAGE UNDERSTANDING SYSTEMS
• Taxonomy
• Descriptions
• Individuals in Natural
• Language Understanding
• The Use of Ooject Special Knowledge
• Natural Language Processing
• Mark H
• Bursteln