TAILIEUCHUNG - Báo cáo khoa học: "Modelling Semantic Role Plausibility in Human Sentence Processing"

We present the psycholinguistically motivated task of predicting human plausibility judgements for verb-role-argument triples and introduce a probabilistic model that solves it. We also evaluate our model on the related role-labelling task, and compare it with a standard role labeller. For both tasks, our model benefits from classbased smoothing, which allows it to make correct argument-specific predictions despite a severe sparse data problem. The standard labeller suffers from sparse data and a strong reliance on syntactic cues, especially in the prediction task. . | Modelling Semantic Role Plausibility in Human Sentence Processing Ulrike Padó and Matthew Crocker Computational Linguistics Saarland University 66041 Saarbrucken Germany ulrike crocker @ Frank Keller School of Informatics University of Edinburgh 2 Buccleuch Place Edinburgh EH8 9LW UK keller@ Abstract We present the psycholinguistically motivated task of predicting human plausibility judgements for verb-role-argument triples and introduce a probabilistic model that solves it. We also evaluate our model on the related role-labelling task and compare it with a standard role labeller. For both tasks our model benefits from classbased smoothing which allows it to make correct argument-specific predictions despite a severe sparse data problem. The standard labeller suffers from sparse data and a strong reliance on syntactic cues especially in the prediction task. 1 Introduction Computational psycholinguistics is concerned with modelling human language processing. Much work has gone into the exploration of sentence comprehension. Syntactic preferences that unfold during the course of the sentence have been successfully modelled using incremental probabilistic context-free parsing models . Jurafsky 1996 Crocker and Brants 2000 . These models assume that humans prefer the most likely structural alternative at each point in the sentence. If the preferred structure changes during processing such models correctly predict processing difficulty for a range of experimentally investigated constructions. They do not however incorporate an explicit notion of semantic processing while there are many phenomena in human sentence processing that demonstrate a non-trivial interaction of syntactic preferences and semantic plausibility. Consider for example the well-studied case of reduced relative clause constructions. When incrementally processing the sentence The deer shot by the hunter was used as a trophy there is a local ambiguity at shot between .

• Báo cáo khoa học
• Modelling Semantic Role Plausibility
• Human Sentence Processing
• Ulrike Padó and Matthew Crocker
• 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
• 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