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A Comparison of fastText Implementations Using Arabic Text Classification

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Intelligent Systems and Applications (IntelliSys 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1038))

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Abstract

The quality of word representation is crucial to obtain good results in many natural language processing tasks. Recently, many word representation models (word embeddings), such as fastText, have been developed. In this research, we compared the algorithms for the fastText implementation, Facebook’s official implementation, and Gensim’s implementation using the same pre-trained fastText model. Using multi-class classification, we evaluated these embeddings. According to the results, the Facebook implementation performed better than Gensim’s implementation, with an average accuracy of 78.22% and 56.73%, respectively, for sentence embeddings and an average accuracy of 79.43% and 57.95%, respectively, for word embeddings.

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Author information

Authors and Affiliations

  1. King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia

    Nuha Alghamdi

  2. University of Jeddah, Jeddah, Kingdom of Saudi Arabia

    Nuha Alghamdi & Fatmah Assiri

Authors
  1. Nuha Alghamdi
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  2. Fatmah Assiri
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Corresponding authors

Correspondence to Nuha Alghamdi or Fatmah Assiri .

Editor information

Editors and Affiliations

  1. School of Computing, Computer Science Research Institute, Ulster University, Newtownabbey, UK

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Alghamdi, N., Assiri, F. (2020). A Comparison of fastText Implementations Using Arabic Text Classification. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_21

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