Deriving a multi-domain information extraction system from a rough ontology

Authors:
Thierry Poibeau
Affiliations:
Thales Research and Technology, Orsay and Laboratoire d'Informatique de Paris-Nord, Villetaneuse, France
Venue:
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Year:
2001

Citing 9
Cited 1

Automatic text processing

Automatic text processing
Little words can make a big difference for text classification

SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Electric words: dictionaries, computers, and meanings

Electric words: dictionaries, computers, and meanings
Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone

SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
An Open Architecture for Multi-Domain Information Extraction

Proceedings of the Thirteenth Conference on Innovative Applications of Artificial Intelligence Conference
Statistical sense disambiguation with relatively small corpora using dictionary definitions

ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Word sense disambiguation using Conceptual Density

COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Word sense disambiguation with very large neural networks extracted from machine readable dictionaries

COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 2
Using information content to evaluate semantic similarity in a taxonomy

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1

Generating extraction patterns from a large semantic network and an untagged corpus

SEMANET '02 Proceedings of the 2002 workshop on Building and using semantic networks - Volume 11

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a multi-domain information extraction system. In order to decrease the time spent on the elaboration of resources for the IE system and guide the end-user in a new domain, we suggest to use a machine learning system that helps defining new templates and associated resources. This knowledge is automatically derived from the text collection, in interaction with the end-user to rapidly develop a local ontology giving an accurate image of the content of the text. The system is finally evaluated using classical indicators.