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Location Recognition with Fuzzy Grammar

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Knowledge Technology (KTW 2011)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 295))

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Abstract

Fuzzy grammar has been introduced as an approach to represent and learn text fragments where the set of learned patterns are represented by combining similar segments to represent regularities and marking interchangeable segments. This paper is dedicated to present a procedural scheme towards learning text fragment in text categorization task facilitated by fuzzy grammars. A few issues are involved in developing fuzzy grammars which are (i) determination of the number of text classes to develop (ii) the selection of text fragments, F relevant to each text class (iii) determining frequent and important terms or keywords, V to develop the set of terminal, T and compound grammars, N. (iv) Conversion of text fragments into grammars, (v) Combination of grammars into a compact form. Comparison between fuzzy grammar and other location entity identifier such as LbjTagger, LingPipe, Newswire and OpenCalais is observed where results have shown that this method outperforms other standard machine learning and statistical-based approach.

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References

  1. Todorovic, B.T., Rancic, S.R., Markovic, I.M., Mulalic, E.H., Ilic, V.M.: Named entity recognition and classification using context Hidden Markov Model. In: 2008 9th Symposium on Neural Network Applications in Electrical Engineering, pp. 43–46 (2008)

    Google Scholar 

  2. Xu, R., Supekar, K., Huang, Y., Das, A., Garber, A.: Combining Text Classification and Hidden Markov Model Techniques for Structuring Randomized Clinical Trial Abstracts. In: Annual Symposium of AMIA, pp. 824–828 (2006)

    Google Scholar 

  3. Chieu, H.L.: Named Entity Recognition: A Maximum Entropy Approach Using Global Information. Language

    Google Scholar 

  4. McCallum, A., Pereira, F., Lafferty, J.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: 18th International Conference on Machine Learning (ICML 2001), pp. 282–289 (2001)

    Google Scholar 

  5. Nadeau, D., Turney, P.D., Matwin, S.: Unsupervised Named-Entity Recognition: Generating Gazetteers and Resolving Ambiguity, pp. 266–277 (2006)

    Google Scholar 

  6. Mikheev, A., Moens, M., Grover, C.: Named Entity Recognition without Gazetteers. Technology (1998)

    Google Scholar 

  7. Watt, A.: Beginning Regular Expressions. Wiley (2005)

    Google Scholar 

  8. Brazma, A.: Learning of Regular Expressions by Pattern Matching. In: Computational Learning Theory, pp. 392–403 (1995)

    Google Scholar 

  9. Martin, T., Shen, Y., Azvine, B.: Incremental Evolution of Fuzzy Grammar Fragments to Enhance Instance Matching and Text Mining. IEEE Transactions on Fuzzy Systems 16, 1425–1438 (2008)

    Article  Google Scholar 

  10. Martin, T., Shen, Y., Azvine, B.: Automated semantic tagging using fuzzy grammar fragments. In: 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence), pp. 2224–2229 (June 2008)

    Google Scholar 

  11. Sharef, N.M., Martin, T., Shen, Y.: Order Independent Incremental Evolving Fuzzy Grammar Fragment Learner. In: Ninth International Conference on Intelligent Systems Design and Applications, pp. 1221–1226 (2009)

    Google Scholar 

  12. Sharef, N.M., Shen, Y.: Text Fragment Extraction using Incremental Evolving Fuzzy Grammar Fragments Learner. In: World Congress on Computational Intelligence, pp. 18–23 (2010)

    Google Scholar 

  13. Sharef, N.M., Martin, T., Shen, Y.: Minimal Combination for Incremental Grammar Fragment Learning (2009)

    Google Scholar 

  14. Sharef, N.M.: Text Fragment Identification with Evolving Fuzzy Grammars (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Intelligent Computing Group, Faculty of Computer Science and Information Technology, University Putra Malaysia, UPM, Serdang, Malaysia

    Nurfadhlina Mohd Sharef

Authors
  1. Nurfadhlina Mohd Sharef
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Editor information

Editors and Affiliations

  1. MIMOS Berhad, Knowledge Technology, Technology Park Malaysia, 57000, Kuala Lumpur, Malaysia

    Dickson Lukose

  2. Department of Systems and Network,College of Information Technology, Jalan IKRAM-UNITEN, Universiti Tenaga Nasional, 43000, Kajang, Malaysia

    Abdul Rahim Ahmad  & Azizah Suliman  & 

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© 2012 Springer-Verlag Berlin Heidelberg

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Mohd Sharef, N. (2012). Location Recognition with Fuzzy Grammar. In: Lukose, D., Ahmad, A.R., Suliman, A. (eds) Knowledge Technology. KTW 2011. Communications in Computer and Information Science, vol 295. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32826-8_26

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  • DOI: https://doi.org/10.1007/978-3-642-32826-8_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32825-1

  • Online ISBN: 978-3-642-32826-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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