Using multiple knowledge sources for word sense discrimination
Computational Linguistics
Using WordNet to disambiguate word senses for text retrieval
SIGIR '93 Proceedings of the 16th annual international ACM SIGIR conference on Research and development in information retrieval
Word sense disambiguation for free-text indexing using a massive semantic network
CIKM '93 Proceedings of the second international conference on Information and knowledge management
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
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
Unsupervised word sense disambiguation rivaling supervised methods
ACL '95 Proceedings of the 33rd annual meeting on Association for Computational Linguistics
Word-sense disambiguation using statistical models of Roget's categories trained on large corpora
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 2
Word sense disambiguation using Conceptual Density
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
Lexical disambiguation using simulated annealing
HLT '91 Proceedings of the workshop on Speech and Natural Language
HLT '93 Proceedings of the workshop on Human Language Technology
Method for WordNet Enrichment Using WSD
TSD '01 Proceedings of the 4th International Conference on Text, Speech and Dialogue
The Role of WSD for Multilingual Natural Language Applications
TSD '02 Proceedings of the 5th International Conference on Text, Speech and Dialogue
Specification Marks for Word Sense Disambiguation: New Development
CICLing '01 Proceedings of the Second International Conference on Computational Linguistics and Intelligent Text Processing
Combining Supervised-Unsupervised Methods for Word Sense Disambiguation
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Specification Marks Method: Design and Implementation
CICLing '02 Proceedings of the Third International Conference on Computational Linguistics and Intelligent Text Processing
Improving the development of data warehouses by enriching dimension hierarchies with WordNet
ODBIS'05/06 Proceedings of the First and Second VLDB conference on Ontologies-based databases and information systems
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Nowadays, the need of advanced free text filtering is increasing. Therefore, when searching for specific keywords, it is desirable to eliminate occurrences where the word or words are used in an inappropriate sense. This task could be exploited in internet browsers, and resource discovery systems, relational databases containing free text fields, electronic document management systems, data warehouse and data mining systems, etc. In order to resolve this problem in this paper a method for the automatic disambiguating of nouns, using the notion of Specification Marks and the noun taxonomy of the WordNet lexical knowledge base [8] is presented. This method is applied to a Natural Language Processing System (NLP). The method resolves the lexical ambiguity of nouns in any sort of text, and although it relies on the semantics relations (Hypernymy/Hyponymy) and the hierarchic organization of WordNet. However, it does not require any sort of training process, no hand-coding of lexical entries, nor the hand-tagging of texts. An evaluation of the method was done on both the Semantic Concordance Corpus (Semcor)[9], and on Microsoft's electronic encyclopaedia ("Microsoft 98 Encarta Encyclopaedia Deluxe"). The percentage of correct resolutions achieved with these two corpora were: Semcor 65.8% and Microsoft 65.6%. This percentages show that successful results with different domain corpus have been obtained, so our proposed method can be applied successfully on any corpus.