tailieunhanh - Báo cáo khoa học: "A Computational Treatment of Sentence"

We describe a computational system which parses discourses consisting of sequences of simple sentences. These contain a range of temporal constructions, including time adverbials, progressive aspect and various aspectual classes. In particular, the grammar generates the required readings, according to the theoretical analysis of (Glasbey, forthcoming), for sentence-final 'then'. | A Computational Treatment of Sentence-Final then Sheila Glasbey Centre for Cognitive Science Edinburgh University 2 Buccleuch Place Edinburgh EH8 9LW UK Abstract We describe a computational system which parses discourses consisting of sequences of simple sentences. These contain a range of temporal constructions including time adverbials progressive aspect and various aspectual classes. In particular the grammar generates the required readings according to the theoretical analysis of Glasbey forthcoming for sentence-final then . 1 Sentence-final then It is possible to follow la Emily climbed Ben Nevis in July. with lb Fiona climbed Snowdon then. This is interpreted to mean that each climb took place at some time within the July in question. Notice however that if we remove in July from la to give lc Emily climbed Ben Nevis. the sequence lc lb becomes harder to interpret and sounds rather The difference is of course that we have removed in July and there is no longer an explicit reference to a time . We will call such an explicitly mentioned time an explicit temporal referent etr . Thus sentence-final then appears 1 We are not concerned here with the rather marginal reading available to some speakers where what is conveyed by lc lb is that Fiona s climb follows Emily s. This corresponds to the updating reading normally associated with sentence-initial then . on the basis of this and other examples to require explicit mention of a time. Being able to infer a time from the description of an event is clearly not enough. We would expect to be able to infer readily from 1c that there was a time at which Emily s climb took place. However it appears that we cannot use sentence-final then here to refer back to such an inferred time. In order to make sense of the sequence lc lb without the ETR it seems we have to be able to see the two events as connected in some way. Consider lc Emily climbed Ben Nevis. ld She achieved her ambition then. which sounds fine and 2a The .

• Báo cáo khoa học
• A Computational Treatment of Sentence
• Sheila Glasbey
• Workshops
• 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
• 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