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An Adaptive Information Retrieval System for Efficient Web Searching

  • Conference paper
Advanced Machine Learning Technologies and Applications (AMLTA 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 488))

Abstract

Stemming algorithms (stemmers) are used to convert the words to their root form (stem), this process is used in the pre-processing stage of the Information Retrieval Systems. The Stemmers affect the indexing time by reducing the size of index file and improving the performance of the retrieval process. There are several stemming algorithms, the most widely used is porter stemming algorithm because of its efficiency, simplicity, speed, and also it easily handles exceptions. However there are some drawbacks, although many attempts were made to improve its structure but it was incomplete. This paper provides an efficient information retrieval technique as well as proposes a new stemming algorithm called Enhanced Porter’s Stemming Algorithm (EPSA). The objective of this technique is to overcome the drawbacks of the porter algorithm and improve the web searching. The EPSA was applied to two datasets to measure its performance. The result shows improvement of the precision over the original porter algorithm while realizing approximately the same recall percentage.

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Author information

Authors and Affiliations

  1. Faculty of Computer & Information Sciences, Ain Shams University, Cairo, Egypt

    Safaa I. Hajeer, Rasha M. Ismail, Nagwa L. Badr & Mohamed Fahmy Tolba

Authors
  1. Safaa I. Hajeer
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  2. Rasha M. Ismail
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  3. Nagwa L. Badr
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  4. Mohamed Fahmy Tolba
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Editor information

Editors and Affiliations

  1. Cairo University, Egypt

    Aboul Ella Hassanien

  2. Ain Shams University, Cairo, Egypt

    Mohamed F. Tolba

  3. Benha University, Benha, Egypt

    Ahmad Taher Azar

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© 2014 Springer International Publishing Switzerland

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Hajeer, S.I., Ismail, R.M., Badr, N.L., Tolba, M.F. (2014). An Adaptive Information Retrieval System for Efficient Web Searching. In: Hassanien, A.E., Tolba, M.F., Taher Azar, A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2014. Communications in Computer and Information Science, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-319-13461-1_44

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  • DOI: https://doi.org/10.1007/978-3-319-13461-1_44

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13460-4

  • Online ISBN: 978-3-319-13461-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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