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International Conference on Active Media Technology

AMT 2014: Active Media Technology pp 1–10Cite as

A Semi-supervised Learning Algorithm for Web Information Extraction with Tolerance Rough Sets

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8610))

Abstract

In this paper, we propose a semi-supervised learning algorithm (TPL) to extract categorical noun phrase instances from unstructured web pages based on the tolerance rough sets model (TRSM). TRSM has been successfully employed for document representation, retrieval and classification tasks. However, instead of the vector-space model, our model uses noun phrases which are described in terms of sets of co-occurring contextual patterns. The categorical information that we employ is derived from the Never Ending Language Learner System (NELL) [3]. The performance of the TPL algorithm is compared with the Coupled Bayesian Sets (CBS) algorithm. Experimental results show that TPL is able to achieve comparable performance with CBS in terms of precision.

This research has been supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery grants 194376. We are very grateful to Prof. Estevam R. Hruschka Jr. and to Saurabh Verma for the NELL dataset and for discussions regarding the NELL project. Special Thanks to Prof. James F. Peters for helpful suggestions.

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Authors and Affiliations

  1. Department of Applied Computer Science, University of Winnipeg, Winnipeg, Manitoba, R3B 2E9, Canada

    Cenker Sengoz & Sheela Ramanna

Authors
  1. Cenker Sengoz
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  2. Sheela Ramanna
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Editor information

Editors and Affiliations

  1. University of Warsaw and Infobright Inc., Poland

    Dominik Ślȩzak

  2. Department of Computer Science, Loughborough University, Loughborough, U.K.

    Gerald Schaefer

  3. Computer Science Department, University of British Columbia, 2366 Main Mall, P.O. Box, Vancouver, B.C., Canada

    Son T. Vuong

  4. Department of Information & Communication Engineering, Inha University, Korea

    Yoo-Sung Kim

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Sengoz, C., Ramanna, S. (2014). A Semi-supervised Learning Algorithm for Web Information Extraction with Tolerance Rough Sets. In: Ślȩzak, D., Schaefer, G., Vuong, S.T., Kim, YS. (eds) Active Media Technology. AMT 2014. Lecture Notes in Computer Science, vol 8610. Springer, Cham. https://doi.org/10.1007/978-3-319-09912-5_1

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  • DOI: https://doi.org/10.1007/978-3-319-09912-5_1

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-09911-8

  • Online ISBN: 978-3-319-09912-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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