Automatic WordNet mapping using word sense disambiguation
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
WordNet: similarity - measuring the relatedness of concepts
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Journal of Web Engineering
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In this paper, an automatic method for Persian WordNet construction based on Prenceton WordNet 2.1 (PWN) is introduced. The proposed approach uses Persian and English corpora as well as a bilingual dictionary in order to make a mapping between PWN synsets and Persian words. Our method calculates a score for each candidate synset of a given Persian word and for each of its translation, it selects the synset with maximum score as a link to the Persian word. The manual evaluation on selected links proposed by our method on 500 randomly selected Persian words, shows about 76.4% quality respect to precision measure. By augmenting the Persian WordNet with the un-ambiguous words, the total accuracy of automatically extracted Persian Word-Net is about 82.6% which outperforms the previously semi-automated generated Persian WordNet by about 12.6%.