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SKOPE: A connectionist/symbolic architecture of spoken Korean processing

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Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing (IJCAI 1995)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1040))

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Abstract

Spoken language processing requires speech and natural language integration. Moreover, spoken Korean calls for novel processing methodology due to its linguistic characteristics. This paper presents SKOPE, a connectionist/symbolic spoken Korean processing engine, emphasizing that: 1) connectionist and symbolic techniques must be selectively applied according to their relative strength and weakness, and 2) linguistic characteristics of Korean must be fully considered for phoneme recognition, speech and language integration, and morphological/ syntactic processing. The design and implementation of SKOPE demonstrates how connectionist/symbolic hybrid architectures can be constructed for spoken agglutinative language processing. Also SKOPE presents many novel ideas for speech and language processing. Phoneme recognition, speech morphological analysis, and syntactic analysis experiments show that SKOPE is a viable approach for spoken Korean processing.

This research was partially supported by KOSEF and ETRI. The SKOPE's various modules were programmed by our students: the phoneme recognition module by Kyunghee Kim, the morphological analysis module by ByungChang Kim & Wonil Lee, and finally the syntax analysis module by Wonil Lee.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Pohang University of Science & Technology, San 31, Hoja-Dong, 790-784, Pohang, Korea

    Geunbae Lee & Jong -Hyeok Lee

Authors
  1. Geunbae Lee
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  2. Jong -Hyeok Lee
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Stefan Wermter Ellen Riloff Gabriele Scheler

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© 1996 Springer-Verlag Berlin Heidelberg

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Lee, G., Lee, J.H. (1996). SKOPE: A connectionist/symbolic architecture of spoken Korean processing. In: Wermter, S., Riloff, E., Scheler, G. (eds) Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing. IJCAI 1995. Lecture Notes in Computer Science, vol 1040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60925-3_41

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  • DOI: https://doi.org/10.1007/3-540-60925-3_41

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  • Print ISBN: 978-3-540-60925-4

  • Online ISBN: 978-3-540-49738-7

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