Similarity-based approaches to natural language processing
Similarity-based approaches to natural language processing
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
The Role of Conceptual Relation in Word Sense Disambiguation
NLDB'01 Proceedings of the 6th International Workshop on Applications of Natural Language to Information Systems
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
Similarity-based word sense disambiguation
Computational Linguistics - Special issue on word sense disambiguation
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Using corpus statistics and WordNet relations for sense identification
Computational Linguistics - Special issue on word sense disambiguation
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
HLT '93 Proceedings of the workshop on Human Language Technology
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
The UNED systems at Senseval-2
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
Semantic tagging using WordNet examples
SENSEVAL '01 The Proceedings of the Second International Workshop on Evaluating Word Sense Disambiguation Systems
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This paper describes a novel method for a word sense disambiguation that utilizes relatives (i.e. synonyms, hypernyms, meronyms, etc in WordNet) of a target word and raw corpora. The method disambiguates senses of a target word by selecting a relative that most probably occurs in a new sentence including the target word. Only one co-occurrence frequency matrix is utilized to efficiently disambiguate senses of many target words. Experiments on several English datum present that our proposed method achieves a good performance.