A statistical approach to sense disambiguation in machine translation
HLT '91 Proceedings of the workshop on Speech and Natural Language
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Natural Language Engineering
An unsupervised method for word sense tagging using parallel corpora
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Ontology-Supported Text Classification Based on Cross-Lingual Word Sense Disambiguation
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
Word Sense Disambiguation of Farsi Homographs Using Thesaurus and Corpus
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
NAACL-Demonstrations '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Demonstration Session
Extended gloss overlaps as a measure of semantic relatedness
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Towards automatic acquisition of a fully sense tagged corpus for persian
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
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Word Sense Disambiguation has long been a central problem in computational linguistics. Word Sense Disambiguation is the ability to identify the meaning of words in context in a computational manner. Statistical and supervised approaches require a large amount of labeled resources as training datasets. In contradistinction to English, the Persian language has neither any semantically tagged corpus to aid machine learning approaches for Persian texts, nor any suitable parallel corpora. Yet due to the ever-increasing development of Persian pages in Wikipedia, this resource can act as a comparable corpus for English-Persian texts. In this paper, we propose a cross-lingual approach to tagging the word senses in Persian texts. The new approach makes use of English sense disambiguators, the Wikipedia articles in both English and Persian, and a newly developed lexical ontology, FarsNet. It overcomes the lack of knowledge resources and NLP tools for the Persian language. We demonstrate the effectiveness of the proposed approach by comparing it to a direct sense disambiguation approach for Persian. The evaluation results indicate a comparable performance to the utilized English sense tagger.