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Arabic Collocation Extraction Based on Hybrid Methods

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Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data (NLP-NABD 2017, CCL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10565))

Abstract

Collocation Extraction plays an important role in machine translation, information retrieval, secondary language learning, etc., and has obtained significant achievements in other languages, e.g. English and Chinese. There are some studies for Arabic collocation extraction using POS annotation to extract Arabic collocation. We used a hybrid method that included POS patterns and syntactic dependency relations as linguistics information and statistical methods for extracting the collocation from Arabic corpus. The experiment results showed that using this hybrid method for extracting Arabic words can guarantee a higher precision rate, which heightens even more after dependency relations are added as linguistic rules for filtering, having achieved 85.11%. This method also achieved a higher precision rate rather than only resorting to syntactic dependency analysis as a collocation extraction method.

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Notes

  1. 1.

    It is worth mentioning that the present study is focused on word pairs, i.e. only lexical collocations containing two words are included. Situations in which the two words are separated are taken into account, but not situations with multiple words.

  2. 2.

    One Arabic word could have more than one from in corpus because Arabic morphology is rich, so has 55 different variants.

  3. 3.

    Bigrams sorted by their dependency score (ds), which actually is the Point Mutual Information Score.

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Author information

Authors and Affiliations

  1. School of Information Science, Beijing Language and Culture University, Beijing, 100083, China

    Alaa Mamdouh Akef, Yingying Wang & Erhong Yang

Authors
  1. Alaa Mamdouh Akef
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  2. Yingying Wang
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  3. Erhong Yang
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Corresponding author

Correspondence to Erhong Yang .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Maosong Sun

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Xiaojie Wang

  3. Peking University, Beijing, China

    Baobao Chang

  4. Soochow University, Suzhou, China

    Deyi Xiong

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Akef, A.M., Wang, Y., Yang, E. (2017). Arabic Collocation Extraction Based on Hybrid Methods. In: Sun, M., Wang, X., Chang, B., Xiong, D. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2017 2017. Lecture Notes in Computer Science(), vol 10565. Springer, Cham. https://doi.org/10.1007/978-3-319-69005-6_1

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  • DOI: https://doi.org/10.1007/978-3-319-69005-6_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69004-9

  • Online ISBN: 978-3-319-69005-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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