Phrase parsing with rule sequence processors: an application to the shared CoNLL task

Authors:
Marc Vilain;David Day
Affiliations:
The MITRE Corporation, Bedford, MA;The MITRE Corporation, Bedford, MA
Venue:
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Year:
2000

Citing 4
Cited 4

A corpus-based approach to language learning

A corpus-based approach to language learning
SPARKLE Work Package 1: Specification of Phrasal Parsing. Final Report

SPARKLE Work Package 1: Specification of Phrasal Parsing. Final Report
Finite-state phrase parsing by rule sequences

COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
MITRE: description of the Alembic system used for MUC-6

MUC6 '95 Proceedings of the 6th conference on Message understanding

Context matching for electronic marketplaces: a case study

The Knowledge Engineering Review
Introduction to the CoNLL-2000 shared task: chunking

ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Peer-to-peer semantic coordination

Web Semantics: Science, Services and Agents on the World Wide Web
UCSG shallow parser

CICLing'06 Proceedings of the 7th international conference on Computational Linguistics and Intelligent Text Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

For several years, chunking has been an integral part of MITRE's approach to information extraction. Our work exploits chunking in two principal ways. First, as part of our extraction system (Alembic) (Aberdeen et al., 1995), the chunker delineates descriptor phrases for entity extraction. Second, as part of our ongoing research in parsing, chunks provide the first level of a stratified approach to syntax - the second level is defined by grammatical relations, much as in the SPARKLE effort (Carroll et al., 1997).