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International Joint Conference on Artificial Intelligence

IJCAI 1995: Connectionist, Statistical and Symbolic Approaches to Learning for Natural Language Processing pp 315–328Cite as

Embedded machine learning systems for natural language processing: A general framework

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

Abstract

This paper presents Kenmore, a general framework for knowledge acquisition for natural language processing (NLP) systems. To ease the acquisition of knowledge in new domains, Kenmore exploits an online corpus using robust sentence analysis and embedded symbolic machine learning techniques while requiring only minimal human intervention. By treating all problems in ambiguity resolution as classification tasks, the framework uniformly addresses a range of subproblems in sentence analysis, each of which traditionally had required a separate computational mechanism. In a series of experiments, we demonstrate the successful use of Kenmore for learning solutions to several problems in lexical and structural ambiguity resolution. We argue that the learning and knowledge acquisition components should be embedded components of the NLP system in that (1) learning should take place within the larger natural language understanding system as it processes text, and (2) the learning components should be evaluated in the context of practical language-processing tasks.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Cornell University, 14853, Ithaca, NY, USA

    Claire Cardie

Authors
  1. Claire Cardie
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Editor information

Stefan Wermter Ellen Riloff Gabriele Scheler

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© 1996 Springer-Verlag Berlin Heidelberg

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Cardie, C. (1996). Embedded machine learning systems for natural language processing: A general framework. 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_56

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  • DOI: https://doi.org/10.1007/3-540-60925-3_56

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60925-4

  • Online ISBN: 978-3-540-49738-7

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