Title
Improving unsupervised WSD with a dynamic thesaurus
Date Issued
07 October 2008
Access level
metadata only access
Resource Type
conference paper
Author(s)
Abstract
The method proposed by Diana McCarthy et al. [1] obtains the predominant sense for an ambiguous word based on a weighted list of terms related to the ambiguous word. This list of terms is obtained using the distributional similarity method proposed by Lin [2] to obtain a thesaurus. In that method, every occurrence of the ambiguous word uses the same thesaurus, regardless of the context where it occurs. Every different word to be disambiguated uses the same thesaurus. In this paper we explore a different method that accounts for the context of a word when determining the most frequent sense of an ambiguous word. In our method the list of distributed similar words is built based on the syntactic context of the ambiguous word. We attain a precision of 69.86%, which is 7% higher than the supervised baseline of using the MFS of 90% SemCor against the remaining 10% of SemCor. © 2008 Springer-Verlag Berlin Heidelberg.
Start page
201
End page
210
Volume
5246 LNAI
Language
English
OCDE Knowledge area
Sistemas de automatización, Sistemas de control
Informática y Ciencias de la Información
Scopus EID
2-s2.0-53049104018
ISSN of the container
16113349
ISBN of the container
3540873902
Conference
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Sponsor(s)
Work done under partial support of Mexican Government (CONACyT, SNI), IPN (PIFI, SIP). The authors wish to thank Rada Mihalcea for her useful comments and discussion.
Sources of information:
Directorio de Producción Científica
Scopus