Rebuilding Lexical Resources for Information Retrieval using Sense Folder Detection and Merging Methods

10221

Authors

nan

Abstract

AbstractIn this paper we discuss the problem of sense disambiguation using lexical resources like ontologies or thesauri with a focus on the application of sense detection and merging methods in information retrieval systems. For an information retrieval task it is important to detect the meaning of a query word for retrieving the related relevant documents. In order to recognize the meaning of a search word, lexical resources, like WordNet, can be used for word sense disambiguation. But, analyzing the WordNet structure, we see that this ontology is fraught with different problems. The too fine grained distinction between word senses, for example, is unfavorable for a usage in information retrieval. We describe related problems and present four implemented online methods to merge SynSets based on relations like hypernyms and hyponyms, and further context information like glosses and domain. Afterwards we show a first evaluation of our approach, compare the different merging methods and discuss briefly future work.

Keywords

nan

Full Content

nan

Labeled Text

AbstractIn this paper we discuss the problem of sense disambiguation using lexical resources like ontologies or thesauri with a focus on the application of sense detection and merging methods in information retrieval systems. For an information retrieval task it is important to detect the meaning of a query word for retrieving the related relevant documents. In order to recognize the meaning of a search word, lexical resources, like WordNet, can be used for word sense disambiguation. But, analyzing the WordNet structure, we see that this ontology is fraught with different problems. The too fine grained distinction between word senses, for example, is unfavorable for a usage in information retrieval. We describe related problems and present four implemented online methods to merge SynSets based on relations like hypernyms and hyponyms, and further context information like glosses and domain. Afterwards we show a first evaluation of our approach, compare the different merging methods and discuss briefly future work.

Publication Details

Paper ID:
10221
Year:
2006
Publication:
TIPSTER
Publication Type:
conf
DOI:
nan