Abstract
Recent works rely on comparable corpora to extract efficient bilingual lexicon. Most of approaches in the litterature for bilingual lexicon extraction are based on context vectors (CV). These approaches suffer from noisy vectors that affect their accuracy. This paper presents new approaches which relies on some advanced text mining methods to extract association rules between terms (AR) and extend them to contextual meta-rules (MR). In this respect, we propose to extract bilingual lexicons by deploying standard context vectors, association rules and contextual meta-rules. These proposed approaches utilize correlations between co-occurrence patterns across language. An experimental validation conducted on a specialized comparable corpora, highlights a significant improvement of bilingual lexicon based on MR compared to the standard approach.
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Notes
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Transliterations are words adapted from a source language to a target language, based on their pronunciation.
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By analogy to the itemset terminology used in data mining for a set of items.
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maxsupp means that the termset must occur at most this user-defined threshold.
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Belhaj Rhouma, S., Latiri, C., Berrut, C. (2023). Advanced Text Mining Methods for Bilingual Lexicon Extraction from Speciliazed Comparable Corpora. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2018. Lecture Notes in Computer Science, vol 13397. Springer, Cham. https://doi.org/10.1007/978-3-031-23804-8_31
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