A web text similarity learning and classification approach for efficient information extraction

  • Authors

    • Sunil Kumar Thota Gitam University, Vishakapatnam, Andhra Pradesh, India
    • Dr. Tummala Sita Mahalakshmi Gitam University, Vishakapatnam
    2019-02-15
    https://doi.org/10.14419/ijet.v7i4.18075
  • Web Mining, Text Similarity, Classification, Information Extraction.

  • Over the last few years, the explosion of the World Wide Web has allowed users to access more and more information. In this circumstance, search engines have become a necessary tool for users to uncover the information they require in a huge space. As a result, the task of organizing this rich information becomes more difficult every day. It plays an important function in accomplishing the information, but numerous of the returned results are not related to the user's necessitates, because they are ranked according to the string match of the user's query. This resulted in semantic differences involved in the meaning of the keywords in the retrieved documents and the terms used in the user's query. The problem of categorizing large sources of information into groups of similar topics has not yet been resolved. In this paper, it proposes a web-text similarity learning (WTSL) method and classification based on SVM mechanism. This proposal aims to automate the estimation of the semantic comparison among the words or article to enhance the information extraction. The experimental results suggest the improvisation towards retrieving more accurate results by retrieving more relevant documents.

     

     

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  • How to Cite

    Kumar Thota, S., & Tummala Sita Mahalakshmi, D. (2019). A web text similarity learning and classification approach for efficient information extraction. International Journal of Engineering & Technology, 7(4), 4856-4861. https://doi.org/10.14419/ijet.v7i4.18075