Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Selective sampling for example-based word sense disambiguation
Computational Linguistics
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
A concept-based adaptive approach to word sense disambiguation
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Subject-dependent co-occurrence and word sense disambiguation
ACL '91 Proceedings of the 29th annual meeting on 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 Conceptual Density
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
| Hi-index | 0.00 |
It is popular in WSD to use contextual information in training sense tagged data. Co-occurring words within a limited window-sized context support one sense among the semantically ambiguous ones of the word. This paper reports on word sense disambiguation of English words using static and dynamic sense vectors. First, context vectors are constructed using contextual words in the training sense tagged data. Then, the words in the context vector are weighted with local density. Using the whole training sense tagged data, each sense of a target word is represented as a static sense vector in word space, which is the centroid of the context vectors. Then contextual noise is removed using a automatic selective sampling. A automatic selective sampling method use information retrieval technique, so as to enhance the discriminative power. In each test case, a automatic selective sampling method retrieves N relevant training samples to reduce noise. Using them, we construct another sense vectors for each sense of the target word. They are called dynamic sense vectors because they are changed according to a target word and its context. Finally, a word sense of a target word is determined using static and dynamic sense vectors. The English SENSEVAL test suit is used for this experimentation and our method produces relatively good results.