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Related Terms Extraction from Arabic News Corpus Using Word Embedding

  • Amina ChouiguiEmail author
  • Oussama Ben Khiroun
  • Bilel Elayeb
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11231)

Abstract

Different techniques are used in text mining to analyze data, extract knowledge, information and relations. We aim in this work to extract related terms for specific keywords. In the first step, we extract Arabic keywords from news articles titles using the TF-IDF terms weighting measure. In the next step, we extract the related terms, from both titles and main texts, using Word2Vec model as a word embedding technique. In order to evaluate our proposed approach, we compute the precision values of the extracted terms that are present in Wikipedia articles. The experiments results perform better for the extracted terms from the articles main texts than titles and the international news category has the highest precision value.

Keywords

Text mining Word embedding Word2Vec Arabic language Terms weighting DL4J Cosine similarity 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Amina Chouigui
    • 1
    • 2
    Email author
  • Oussama Ben Khiroun
    • 1
    • 2
  • Bilel Elayeb
    • 1
    • 3
  1. 1.RIADI Research Laboratory, ENSIManouba UniversityManoubaTunisia
  2. 2.National Engineering School of Sousse, ENISOSousse UniversitySousseTunisia
  3. 3.Emirates College of TechnologyAbu DhabiUnited Arab Emirates

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