LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting

Proceedings of the 11th International Workshop on Semantic Evaluations (SemEval-2017), pages 134–138, Vancouver, Canada, August 3 - 4, 2017. c⃝2017 Association for Computational Linguistics LIM-LIG at SemEval-2017 Task1: Enhancing the Semantic Similarity for Arabic Sentences with Vectors Weighting El Moatez Billah Nagoudi Laboratoire d’Informatique et de Math´ematiques LIM Universit´e Amar Telidji de Laghouat, Alg´erie e.nagoudi@lagh-univ.dz J´er´emy Ferrero Compilatio 276 rue du Mont Blanc 74540 Saint-F´elix, France LIG-GETALP Univ. Grenoble Alpes, France jeremy.ferrero@imag.fr Didier Schwab LIG-GETALP Univ. Grenoble Alpes France didier.schwab@imag.fr Abstract This article describes our proposed sys- tem named LIM-LIG. This system is de- signed for SemEval 2017 Task1: Seman- tic Textual Similarity (Track1). LIM-LIG proposes an innovative enhancement to word embedding-based model devoted to measure the semantic similarity in Ara- bic sentences. The main idea is to exploit the word representations as vectors in a multidimensional space to capture the se- mantic and syntactic properties of words. IDF weighting and Part-of-Speech tagging are applied on the examined sentences to support the identification of words that are highly descriptive in each sentence. LIM-LIG system achieves a Pearsons cor- relation of 0.74633, ranking 2nd among all participants in the Arabic monolingual pairs STS task organized within the Se- mEval 2017 evaluation campaign. 1 Introduction Semantic Textual Similarity (STS) is an important task in several application fields, such as infor- mation retrieval, machine translation, plagiarism detection and others. STS measures the degree of similarity between the meanings of two text sequences (Agirre et al., 2015). Since SemEval 2013, STS has been one of the official shared tasks. This is the first year in which SemEval has orga- nized an Arabic monolingual pairs STS. The chal- lenge in this task lies in the interpretation of the semantic similarity of two given Arabic sentences, with a continuous valued score ranging from 0 to 5. The Arabic STS measurement could be very useful for several areas, including: disguised pla- giarism detection, word-sense disambiguation, la- tent semantic analysis (LSA) or paraphrase identi- fication. A very important advantage of SemEval evaluation campaign, is enabling the evaluation of several different systems on a common datasets. Which makes it possible to produce a novel an- notated datasets that can be used in future NLP research. In this article we present our LIM-LIG sys- tem devoted to enhancing the semantic similarity between Arabic sentences. In STS task (Arabic monolingual pairs) SemEval 2017, the LIM-LIG system propose three methods to measure this sim- ilarity: No weighting, IDF weighting and Part- of-speech weighting Method. The best submit- ted method (Part-of-speech weighting) achieves a Pearsons correlation of 0.7463, ranking 2nd in the Arabic monolingual STS task. In addition, we have proposed another method (after the compe- tition) named Mixed method, with this method, the correlation rate reached 0.7667, which repre- sent the best score among the different submitted methods involved in the Arabic monolingual STS task. 2 Word Embedding Models In the literature, several techniques are proposed to build word-embedding model. For instance, Collobert and Weston (2008) have proposed a unified system based …

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