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Unsupervised Learning of Pattern Templates from Unannotated Corpora for Proper Noun Extraction

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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2639))

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Abstract

This paper describes an approach to extracting proper nouns in the very large text corpora without using the lexicon or cue word dictionary. At first, we train the pattern for extracting the proper nouns by applying the initial proper names into the unannotated corpora that does not have any tags yet. And then we continuously apply the pattern templates into the corpora in order to extract new proper nouns until certain period.

This work was supported by the Korea Science and Engineering Foundation(KOSEF) through the Advanced Information Technology Research Center(AITrc).

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References

  1. MUC, Proc. of 7th Message Understanding Conference(MUC-7), (1998)

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  2. Borthwick, A.: A Japanese Named Entity Recognizer Constructed by a Non-speaker of Japanese. In Proc. of the IREX Workshop (1999) 187–193

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  3. Yangaber, R., W. Lin, and R. Grishman: Unsupervised Learning of Generalized Names. In Proc. of the 19 th International Conference on Computational Linguistics, (2002) 1135–1141

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  4. Stevenson, M. and R. Gaizauskas: Improving Named Entity Recognition using Annotated Corpora. LREC Workshop on Information Extraction meets Corpus Linguistics (2000)

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  5. Kang, S.: Korean Morphological Analyzer. http://nlp.kookmin.ac.kr/ (2000)

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Author information

Authors and Affiliations

  1. School of Computer Science, Kookmin University & AITrc, Seoul, 136-702, Korea

    Seung-Shik Kang & Chong-Woo Woo

Authors
  1. Seung-Shik Kang
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  2. Chong-Woo Woo
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Editor information

Editors and Affiliations

  1. Institute of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, P.R. China

    Guoyin Wang

  2. Department of Computer Science, Nanchang University, Nanchang, 330029, P.R. China

    Qing Liu

  3. Department of Computer Science, University of Regina, Regina, Saskatchewan, S4S 0A2, Canada

    Yiyu Yao

  4. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

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© 2003 Springer-Verlag Berlin Heidelberg

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Kang, SS., Woo, CW. (2003). Unsupervised Learning of Pattern Templates from Unannotated Corpora for Proper Noun Extraction. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_103

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  • DOI: https://doi.org/10.1007/3-540-39205-X_103

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-14040-5

  • Online ISBN: 978-3-540-39205-7

  • eBook Packages: Springer Book Archive

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