An assumptive logic programming methodology for parsing

K. Voll, T. Yeh, V. Dahl

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

We show how several novel tools in logic programming for AI (namely, continuation based linear and timeless assumptions, and datalog grammars) can assist us in producing terse treatments of difficult language processing phenomena. As a proof of concept, we present a concise parser for datalog grammars (logic grammars where strings are represented with numbered word boundaries rather than as lists of words), that uses assumptions and a combination of left-corner parsing and charting. We then study two test cases of this parser's application: complete constituent coordination, and error diagnosis and correction.

Original languageEnglish
Title of host publicationProceedings - 12th IEEE Internationals Conference on Tools with Artificial Intelligence, ICTAI 2000
PublisherIEEE Computer Society
Pages11-18
Number of pages8
ISBN (Electronic)0769509096
DOIs
StatePublished - 2000
Event12th IEEE Internationals Conference on Tools with Artificial Intelligence, ICTAI 2000 - Vancouver, Canada
Duration: 13 Nov 200015 Nov 2000

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
Volume2000-January
ISSN (Print)1082-3409

Conference

Conference12th IEEE Internationals Conference on Tools with Artificial Intelligence, ICTAI 2000
Country/TerritoryCanada
CityVancouver
Period13/11/0015/11/00

Bibliographical note

Publisher Copyright:
© 2000 IEEE.

Keywords

  • Artificial intelligence
  • Automatic programming
  • Databases
  • Error correction
  • Functional programming
  • Logic programming
  • Natural language processing
  • Natural languages
  • Testing

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