An experimental study of factors important in document ranking
Proceedings of the 9th annual international ACM SIGIR conference on Research and development in information retrieval
SIGDOC '86 Proceedings of the 5th annual international conference on Systems documentation
Introduction to the special issue on word sense disambiguation: the state of the art
Computational Linguistics - Special issue on word sense disambiguation
Word sense disambiguation using Conceptual Density
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
An unsupervised method for word sense tagging using parallel corpora
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
An HMM approach to vowel restoration in Arabic and Hebrew
SEMITIC '02 Proceedings of the ACL-02 workshop on Computational approaches to semitic languages
Word Sense Disambiguation: Algorithms and Applications (Text, Speech and Language Technology)
Word Sense Disambiguation: Algorithms and Applications (Text, Speech and Language Technology)
Maximum entropy based restoration of Arabic diacritics
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Word sense disambiguation: A survey
ACM Computing Surveys (CSUR)
A fully unsupervised word sense disambiguation method using dependency knowledge
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Arabic diacritization using weighted finite-state transducers
Semitic '05 Proceedings of the ACL Workshop on Computational Approaches to Semitic Languages
Contribution to semantic analysis of Arabic language
Advances in Artificial Intelligence
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In this paper, we propose to use Harman, Croft and Okapi measures with Lesk algorithm to develop a system for Arabic word sense disambiguation, that combines unsupervised and knowledge based methods. This system must solve the lexical semantic ambiguity in Arabic language. The information retrieval measures are used to estimate the most relevant sense of the ambiguous word, by returning a semantic coherence score corresponding to the context that is semantically closest to the original sentence containing the ambiguous word. The Lesk algorithm is used to assign and select the adequate sense from those proposed by the information retrieval measures mentioned above. This selection is based on a comparison between the glosses of the word to be disambiguated, and its different contexts of use extracted from a corpus. Our experimental study proves that using of Lesk algorithm with Harman, Croft, and Okapi measures allows us to obtain an accuracy rate of 73%.