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Word Embedding-Based Approaches for Measuring Semantic Similarity of Arabic-English Sentences

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Arabic Language Processing: From Theory to Practice (ICALP 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 782))

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Abstract

Semantic Textual Similarity (STS) is an important component in many Natural Language Processing (NLP) applications, and plays an important role in diverse areas such as information retrieval, machine translation, information extraction and plagiarism detection. In this paper we propose two word embedding-based approaches devoted to measuring the semantic similarity between Arabic-English cross-language sentences. The main idea is to exploit Machine Translation (MT) and an improved word embedding representations in order to capture the syntactic and semantic properties of words. MT is used to translate English sentences into Arabic language in order to apply a classical monolingual comparison. Afterwards, two word embedding-based methods are developed to rate the semantic similarity. Additionally, Words Alignment (WA), Inverse Document Frequency (IDF) and Part-of-Speech (POS) weighting are applied on the examined sentences to support the identification of words that are most descriptive in each sentence. The performances of our approaches are evaluated on a cross-language dataset containing more than 2400 Arabic-English pairs of sentence. Moreover, the proposed methods are confirmed through the Pearson correlation between our similarity scores and human ratings.

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Notes

  1. 1.

    http://eurovoc.europa.eu/.

  2. 2.

    https://sites.google.com/site/mohazahran/data.

  3. 3.

    https://cloud.google.com/translate/.

  4. 4.

    http://alt.qcri.org/semeval2017/task1/index.php?id=data-and-tools.

  5. 5.

    http://alt.qcri.org/semeval2017/task1/data/uploads/sts2017.eval.v1.1.zip.

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Author information

Authors and Affiliations

  1. Laboratoire d’Informatique et de Mathématique LIM, Amar Telidji University, Laghouat, Algeria

    El Moatez Billah Nagoudi & Hadda Cherroun

  2. Compilatio, 276 rue du Mont Blanc, 74540, Saint-Félix, France

    Jérémy Ferrero

  3. LIG-GETALP, Univ. Grenoble Alpes, Grenoble, France

    Jérémy Ferrero & Didier Schwab

Authors
  1. El Moatez Billah Nagoudi
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  2. Jérémy Ferrero
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  3. Didier Schwab
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  4. Hadda Cherroun
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Correspondence to El Moatez Billah Nagoudi .

Editor information

Editors and Affiliations

  1. Ex ENSA-USMBA, Fez, Morocco

    Abdelmonaime Lachkar

  2. EMI, UM5, Rabat, Morocco

    Karim Bouzoubaa

  3. FS, UMP, Oujda, Morocco

    Azzedine Mazroui

  4. IERA, UM5, Rabat, Morocco

    Abdelfettah Hamdani

  5. FS, UMP, Oujda, Morocco

    Abdelhak Lekhouaja

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Nagoudi, E.M.B., Ferrero, J., Schwab, D., Cherroun, H. (2018). Word Embedding-Based Approaches for Measuring Semantic Similarity of Arabic-English Sentences. In: Lachkar, A., Bouzoubaa, K., Mazroui, A., Hamdani, A., Lekhouaja, A. (eds) Arabic Language Processing: From Theory to Practice. ICALP 2017. Communications in Computer and Information Science, vol 782. Springer, Cham. https://doi.org/10.1007/978-3-319-73500-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-73500-9_2

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