Similarity-based word sense disambiguation: Word sense disambiguation
We describe a method for automatic word sense disambiguation using a text corpus and a machine-readble dictionary (MRD). The method is based on word similarity and context similarity measures. Words are considered similar if they appear in similar contexts; contexts are similar if they contain simil...
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| Veröffentlicht in: | Computational linguistics - Association for Computational Linguistics 1998-03, Vol.24 (1), p.41-59 |
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| container_title | Computational linguistics - Association for Computational Linguistics |
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| creator | KAROV, Y EDELMAN, S |
| description | We describe a method for automatic word sense disambiguation using a text corpus and a machine-readble dictionary (MRD). The method is based on word similarity and context similarity measures. Words are considered similar if they appear in similar contexts; contexts are similar if they contain similar words. The circularity of this definition is resolved by an iterative, converging process, in which the system learns from the corpus a set of typical usages for each of the senses of the polysemous word listed in the MRD. A new instance of a polysemous word is assigned the sense associated with the typical usage most similar to its context. Experiments show that this method can learn even from very sparse training data, achieving over 92% correct disambiguation performance. |
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| identifier | ISSN: 0891-2017 |
| ispartof | Computational linguistics - Association for Computational Linguistics, 1998-03, Vol.24 (1), p.41-59 |
| issn | 0891-2017 1530-9312 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_85502470 |
| source | Alma/SFX Local Collection |
| subjects | Applied linguistics Computational linguistics Linguistics |
| title | Similarity-based word sense disambiguation: Word sense disambiguation |
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