Using Earth Mover's Distance and Word Embeddings for Recognizing Textual Entailment in Arabic

Tarik Boudaa, Mohamed El Marouani, Nourddine Enneya


Recognizing Textual Entailment (RTE) is a task of Natural Language Processing (NLP) in which two texts denoted TEXT (T) and HYPOTHESIS (H) are processed by a system to determine whether the meaning of H is inferred (entailed) from T or not. This task is useful for several NLP applications and it has attracted a lot of attention in research. Most of studies focused on English as a target language. In this paper, we give an overview of the main studies on Textual Entailment for English and Arabic and we present a new approach to deal with this task for Arabic using a measure of similarity based on Earth Mover's Distance, and machine learning. We experimented this approach using state of the art Arabic NLP tools and we achieved encouraging results. Although we have applied this approach only to Arabic, its application to other languages is still possible.


Recognizing Textual Entailment (RTE), Natural Language Inference (NLI), Arabic NLP, earth mover's distance, machine learning

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