A Proposal for word sense disambiguation using conceptual distance

This paper presents a method for the resolution of lexical ambiguity and its automatic evaluation over the Brown Corpus. The method relies on the use of the wide-coverage noun taxonomy of WordNet and the notion of conceptual distance among concepts, captured by a Conceptual Density formula developed for this purpose. This fully automatic method requires no hand coding of lexical entries, hand tagging of text nor any kind of training process. The results of the experiment have been automatically evaluated against SemCor, the sense-tagged version of the Brown Corpus.Postprint (published version

Indexing with WordNet synsets can improve Text Retrieval

The classical, vector space model for text retrieval is shown to give better results (up to 29% better in our experiments) if WordNet synsets are chosen as the indexing space, instead of word forms. This result is obtained for a manually disambiguated test collection (of queries and documents) derived from the Semcor semantic concordance. The sensitivity of retrieval performance to (automatic) disambiguation errors when indexing documents is also measured. Finally, it is observed that if queries are not disambiguated, indexing by synsets performs (at best) only as good as standard word indexing.Comment: 7 pages, LaTeX2e, 3 eps figures, uses epsfig, colacl.st

LabelTranslator: A Tool to Automatically Localize an Ontology

This demo proposal briefly presents LabelTranslator, a system that suggests translations of ontology labels, with the purpose of localizing ontologies. LabelTranslator takes as input an ontology whose labels are described in a source natural language and obtains the most probable translation of each ontology label into a target natural language.Our main contribution is the automatization of this process, which reduces human efforts to localize manually the ontology