Word association norms, mutual information, and lexicography
Computational Linguistics
High performance question/answering
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Discovering word senses from text
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Discovering corpus-specific word senses
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 2
Discriminating among word senses using McQuitty's similarity analysis
NAACLstudent '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Proceedings of the HLT-NAACL 2003 student research workshop - Volume 3
Automatic Detection of Terminology Evolution
OTM '09 Proceedings of the Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: ADI, CAMS, EI2N, ISDE, IWSSA, MONET, OnToContent, ODIS, ORM, OTM Academy, SWWS, SEMELS, Beyond SAWSDL, and COMBEK 2009
Using word sense discrimination on historic document collections
Proceedings of the 10th annual joint conference on Digital libraries
Knowing where and how criminal organizations operate using web content
Proceedings of the 21st ACM international conference on Information and knowledge management
Towards mobile language evolution exploitation
Multimedia Tools and Applications
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Lexico-semantic networks such as WordNet have been criticized about the nature of the senses they distinguish as well as on the way they define these senses. In this article, we present a possible solution to overcome these limits by defining the sense of words from the way they are used. More precisely, we propose to differentiate the senses of a word from a network of lexical cooccurrences built from a large corpus. This method was tested both for French and English and was evaluated for English by comparing its results with WordNet.