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Exploring and exploiting a historical corpus for Arabic

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Abstract

This paper presents a historical Arabic corpus named HAC. At this early embryonic stage of the project, we report about the design, the architecture and some of the experiments which we have conducted on HAC. The corpus, and accordingly the search results, will be represented using a primary XML exchange format. This will serve as an intermediate exchange tool within the project and will allow the user to process the results offline using some external tools. HAC is made up of Classical Arabic texts that cover 1600 years of language use; the Quranic text, Modern Standard Arabic texts, as well as a variety of monolingual Arabic dictionaries. The development of this historical corpus assists linguists and Arabic language learners to effectively explore, understand, and discover interesting knowledge hidden in millions of instances of language use. We used techniques from the field of natural language processing to process the data and a graph-based representation for the corpus. We provided researchers with an export facility to render further linguistic analysis possible.

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Notes

  1. http://nlp.stanford.edu/software/tagger.shtml.

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Acknowledgments

The authors would like to thank the graduate students of the Linguistics Department at the University of Jordan for their help in compiling the Arabic historical corpus. Also we would like to sincerely thank the anonymous reviewers of the first submission for their thoughtful comments to enhance our work.

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Authors and Affiliations

  1. Computer Information Systems Department, King Abdullah II School for Information Technology, University of Jordan, Amman, Jordan

    Bassam Hammo, Omaima Ismail & Mohammad AbuShariah

  2. Linguistics Department, University of Jordan, Amman, Jordan

    Sane Yagi

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  1. Bassam Hammo
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  2. Sane Yagi
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  3. Omaima Ismail
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  4. Mohammad AbuShariah
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Correspondence to Bassam Hammo.

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Hammo, B., Yagi, S., Ismail, O. et al. Exploring and exploiting a historical corpus for Arabic. Lang Resources & Evaluation 50, 839–861 (2016). https://doi.org/10.1007/s10579-015-9304-9

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