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Information retrieval on Turkish texts

Author

Listed:
  • Fazli Can
  • Seyit Kocberber
  • Erman Balcik
  • Cihan Kaynak
  • H. Cagdas Ocalan
  • Onur M. Vursavas

Abstract

In this study, we investigate information retrieval (IR) on Turkish texts using a large‐scale test collection that contains 408,305 documents and 72 ad hoc queries. We examine the effects of several stemming options and query‐document matching functions on retrieval performance. We show that a simple word truncation approach, a word truncation approach that uses language‐dependent corpus statistics, and an elaborate lemmatizer‐based stemmer provide similar retrieval effectiveness in Turkish IR. We investigate the effects of a range of search conditions on the retrieval performance; these include scalability issues, query and document length effects, and the use of stopword list in indexing.

Suggested Citation

  • Fazli Can & Seyit Kocberber & Erman Balcik & Cihan Kaynak & H. Cagdas Ocalan & Onur M. Vursavas, 2008. "Information retrieval on Turkish texts," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(3), pages 407-421, February.
    • Handle: RePEc:bla:jamist:v:59:y:2008:i:3:p:407-421
    DOI: 10.1002/asi.20750
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    Cited by:

    1. Migena Ceyhan & Zeynep Orhan & Dimitrios Karras, 2020. "An Approach for Movie Review Classification in Turkish," European Journal of Engineering and Formal Sciences Articles, European Center for Science Education and Research, vol. 4, May - Aug.
    2. repec:eur:ejfejr:43 is not listed on IDEAS
    3. Cagri Toraman & Fazli Can, 2017. "Discovering story chains: A framework based on zigzagged search and news actors," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(12), pages 2795-2808, December.

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