WordNet and Wiktionary-Based Approach for Word Sense Disambiguation
Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered as a task whose solution is at least as hard as the most difficult problems in artificial intelligence. This is basically used in application like information retri...
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Published in | Transactions on Computational Collective Intelligence XXIX Vol. 10840; pp. 123 - 143 |
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Main Authors | , , |
Format | Book Chapter |
Language | English |
Published |
Switzerland
Springer International Publishing AG
2018
Springer International Publishing |
Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
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Summary: | Word sense disambiguation (WSD) is the ability to identify the meaning of words in context in a computational manner. WSD is considered as a task whose solution is at least as hard as the most difficult problems in artificial intelligence. This is basically used in application like information retrieval, machine translation, information extraction because of its semantics understanding. This paper describes the proposed approach W3SD (This paper is an extended version of our work [4] published in the 8th International Conference on Computational Collective Intelligence.) which is based on the words surrounding the polysemous word in a context. Each meaning of these words is represented by a vector composed of weighted nouns using WordNet and Wiktionary features through the taxonomic information content from WordNet and the glosses from Wiktionary. The main emphasis of this paper is feature selection for disambiguation purpose. The assessment of WSD systems is discussed in the context of the Senseval campaign, aiming at the objective evaluation of our proposal to the systems participating in several different disambiguation tasks. |
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ISBN: | 3319902865 9783319902869 |
ISSN: | 0302-9743 1611-3349 |
DOI: | 10.1007/978-3-319-90287-6_7 |