Wordnet based word sense disambiguation

Authors:
Andrzej Siemiński
Affiliations:
Institute of Informatics, Wroclaw University of Technology, Wrocław, Poland
Venue:
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part II
Year:
2011

Citing 6
Cited 0

A vector space model for automatic indexing

Communications of the ACM
An Iterative Approach to Word Sense Disambiguation

Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference
The interaction of knowledge sources in word sense disambiguation

Computational Linguistics
Unsupervised sense disambiguation using bilingual probabilistic models

ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Introduction to Information Retrieval

Introduction to Information Retrieval
Corpus-based and knowledge-based measures of text semantic similarity

AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1

Quantified Score

Hi-index	0.00

Visualization

Abstract

Due to the intrinsic ambiguity of a natural language the word sense disambiguation or WSD is a challenging task. The paper uses WordNet for (WSD) for that purpose. Unlike many others approaches on that area it exploits the structure of WordNet in an indirect manner. To disambiguate the words it measures the semantic similarity of the words glosses. The similarity is calculated using the SynPath algorithm. Its essence is the replacement of each word by a sequence of WordNet synset identifiers that describe related concepts. To measure the similarity of such sequence the standard tf-idf formula is used. At the last stage a modification of Ant Colony Optimization for the Traveling Salesman Problem is responsible for word disambiguation.