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Acquisition of Domain Knowledge

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Information Extraction in the Web Era (SCIE 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2700))

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Abstract

Linguistic knowledge in Natural Language understanding systems is commonly stratified across several levels. This is true of Information Extraction as well. Typical state-of-the-art Information Extraction systems require syntactic-semantic patterns for locating facts or events in text; domain-specific word or concept classes for semantic generalization; and a specialized lexicon of terms that may not be found in general-purpose dictionaries, among other kinds of knowledge.

We describe an approach to unsupervised, or minimally supervised, knowledge acquisition. The approach is based on bootstrapping a comprehensive knowledge base from a small set of seed elements. Our approach is embodied in algorithms for discovery of patterns, concept classes, and lexicon, from raw un-annotated text.

We present the results of knowledge acquisition, and examine them in the context of prior work. We discuss problems in evaluating the quality of the acquired knowledge, and methodologies for evaluation.

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Authors and Affiliations

  1. Courant Institute of Mathematical Sciences, New York University, New York, USA

    Roman Yangarber

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  1. Roman Yangarber
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Editors and Affiliations

  1. DISP, University of Tor Vergata, Via del Politecnico 1, Rome, Italy

    Maria Teresa Pazienza

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© 2003 Springer-Verlag Berlin Heidelberg

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Yangarber, R. (2003). Acquisition of Domain Knowledge. In: Pazienza, M.T. (eds) Information Extraction in the Web Era. SCIE 2002. Lecture Notes in Computer Science(), vol 2700. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45092-4_1

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  • DOI: https://doi.org/10.1007/978-3-540-45092-4_1

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  • Print ISBN: 978-3-540-40579-5

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