An experiment in computational discrimination of English word senses
IBM Journal of Research and Development
Proceedings of the 1992 ACM/IEEE conference on Supercomputing
WordNet: a lexical database for English
Communications of the ACM
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Using WordNet and Lexical Operators to Improve Internet Searches
IEEE Internet Computing
Lexical Enrichment of WordNet with Classification Systems Using Specification Marks Method
NLDB'01 Proceedings of the 6th International Workshop on Applications of Natural Language to Information Systems
An Iterative Approach to Word Sense Disambiguation
Proceedings of the Thirteenth International Florida Artificial Intelligence Research Society Conference
Natural Language Engineering
Combining unsupervised lexical knowledge methods for word sense disambiguation
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Using syntactic dependency as local context to resolve word sense ambiguity
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Word sense disambiguation using optimised combinations of knowledge sources
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Error driven word sense disambiguation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Integrating multiple knowledge sources to disambiguate word sense: an exemplar-based approach
ACL '96 Proceedings of the 34th 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
Role of word sense disambiguation in lexical acquisition: predicting semantics from syntactic cues
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
A method for word sense disambiguation of unrestricted text
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Combining weak knowledge sources for sense disambiguation
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - 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
Semantic tagging using WordNet examples
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Using domain information for word sense disambiguation
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
The University of Alicante word sense disambiguation system
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Automatic WSD: does it make sense of Estonian?
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
IdentityRank: Named entity disambiguation in the news domain
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
In this article, we concentrate in conceptual relations as a source of information for Word Sense Disambiguation (WSD) systems. We start with a review the most relevant research in the field, then we implement our own algorithm. As a starting point we have chosen the conceptual density algorithm of Agirre and Rigau. We generalize the original algorithm, parameterizing many aspects. This new algorithm obtains a relative improvement of 24% in terms of precision and recall. We also offer comparative evaluation of our system with respect to the participants in the SENSEVAL-2 disambiguation competition. We conclude that conceptual relations provide a source of information that is insufficient by itself to achieve good disambiguation results, but can, however, be a very accurate heuristic in a combined system.