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A Hybrid Approach for Extracting Arabic Persons’ Names and Resolving Their Ambiguity from Twitter

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Natural Language Processing and Information Systems (NLDB 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9103))

Abstract

Tweets offer a novel way of communication that enables users all over the world to share real-time news and ideas. The massive amount of tweets, generated regularly by Arabic speakers, has resulted in a growing interest in building Arabic named entity recognition (NER) systems that deal with the informal colloquial Arabic. The unique characteristics of the Arabic language make Arabic NER a challenging task, which, the informal nature of tweets further complicates. The majority of previous works addressing Arabic NER were concerned with formal modern standard Arabic (MSA). Moreover, taggers and parsers were often utilized to solve the ambiguity problem of Arabic persons’ names. Although, previously developed approaches perform well on MSA text, they are not suited for colloquial Arabic. This paper introduces a hybrid approach to extract Arabic persons’ names from tweets in addition to a way to resolve their ambiguity using context bigram patterns. The introduced approach attempts not to use any language-dependent resources. Evaluation of the presented approach shows a 7 % improvement in the F-score over the best reported result in the literature.

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Notes

  1. 1.

    Diacritics are special marks placed above or below the letters.

  2. 2.

    http://tmrg.nileu.edu.eg/downloads.html (under the title: Persons’ Names Dictionaries).

  3. 3.

    The rest is used to collect a test dataset.

  4. 4.

    Waikato Environment for Knowledge Analysis.

  5. 5.

    We experimented with SVM and BayesNET. Although the results in the classification phase were better but they were significantly lower in the extraction phase.

  6. 6.

    The authors omitted the organizations’ names class because it has small frequency in the annotated data.

  7. 7.

    LDC2012T09: GALE Arabic-Dialect/English Parallel Text.

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Author information

Authors and Affiliations

  1. Center of Informatics Science, Nile University, Giza, Egypt

    Omnia H. Zayed & Samhaa R. El-Beltagy

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  1. Omnia H. Zayed
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  2. Samhaa R. El-Beltagy
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Correspondence to Omnia H. Zayed .

Editor information

Editors and Affiliations

  1. Technische Universität Darmstadt, Darmstadt, Germany

    Chris Biemann

  2. Universität Passau, Passau, Germany

    Siegfried Handschuh

  3. Universität Passau, Passau, Germany

    André Freitas

  4. University of Salford, Salford, United Kingdom

    Farid Meziane

  5. Conservatoire National des Arts et Métiers, Paris, France

    Elisabeth Métais

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Zayed, O.H., El-Beltagy, S.R. (2015). A Hybrid Approach for Extracting Arabic Persons’ Names and Resolving Their Ambiguity from Twitter. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2015. Lecture Notes in Computer Science(), vol 9103. Springer, Cham. https://doi.org/10.1007/978-3-319-19581-0_32

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  • DOI: https://doi.org/10.1007/978-3-319-19581-0_32

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