Abstract
Many methods for measuring the semantic similarity between sentences have been proposed, particularly for English. These methods are considered restrictive as they usually do not take into account some semantic and syntactic-semantic knowledge like semantic predicate, thematic role and semantic class. Measuring the semantic similarity between sentences in Arabic is particularly a challenging task because of the complex linguistic structure of the Arabic language and given the lack of electronic resources such as syntactic-semantic knowledge and annotated corpora.
In this paper, we proposed a method for measuring Arabic sentences’ similarity based on automatic learning taking advantage of LMF standardized Arabic dictionaries, notably the syntactic-semantic knowledge that they contain. Furthermore, we evaluated our proposal with the cross validation method by using 690 pairs of sentences taken from old Arabic dictionaries designed for human use like Al-Wassit and Lissan-Al-Arab. The obtained results are very encouraging and show a good performance that approximates to human intuition.
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Wali, W., Gargouri, B., hamadou, A.B. (2015). Supervised Learning to Measure the Semantic Similarity Between Arabic Sentences. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9329. Springer, Cham. https://doi.org/10.1007/978-3-319-24069-5_15
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DOI: https://doi.org/10.1007/978-3-319-24069-5_15
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