Abstract
In this paper, we present an adaptation to Modern Standard Arabic of a French and English term extractor. The goal of this work is to reduce the lack of resources and NLP tools for Arabic language in specialised domains. The adaptation firstly focuses on the description of extraction processes similar to those already defined for French and English while considering the morpho-syntactic specificity of Arabic. Agglutination phenomena are further taken into account in the term extraction process. The current state of the adapted system was evaluated on a medical text corpus. 400 maximal candidate terms were examined, among which 288 were correct (72% precision). An error analysis shows that term extraction errors are first due to Part-of-Speech tagging errors and the difficulties induced by non-diacritised texts, then to remaining agglutination phenomena.
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Notes
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noun-m-s-g-d: defined singular masculine noun in genitive case. noun-f-s-n-c: constructed singular feminine noun in nominative case.
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Neifar, W., Hamon, T., Zweigenbaum, P., Khemakhem, M.E., Belguith, L.H. (2018). Adaptation of a Term Extractor to Arabic Specialised Texts: First Experiments and Limits. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2016. Lecture Notes in Computer Science(), vol 9623. Springer, Cham. https://doi.org/10.1007/978-3-319-75477-2_16
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