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Reviewing and Evaluating Automatic Term Recognition Techniques

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Advances in Natural Language Processing (GoTAL 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5221))

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Abstract

Automatic Term Recognition (ATR) is defined as the task of identifying domain specific terms from technical corpora. Termhood-based approaches measure the degree that a candidate term refers to a domain specific concept. Unithood-based approaches measure the attachment strength of a candidate term constituents. These methods have been evaluated using different, often incompatible evaluation schemes and datasets. This paper provides an overview and a thorough evaluation of state-of-the-art ATR methods, under a common evaluation framework, i.e. corpora and evaluation method. Our contributions are two-fold: (1) We compare a number of different ATR methods, showing that termhood-based methods achieve in general superior performance. (2) We show that the number of independent occurrences of a candidate term is the most effective source for estimating term nestedness, improving ATR performance.

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Authors and Affiliations

  1. Department of Computer Science, The University of York, Heslington, York, YO10 5NG, UK

    Ioannis Korkontzelos, Ioannis P. Klapaftis & Suresh Manandhar

Authors
  1. Ioannis Korkontzelos
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  2. Ioannis P. Klapaftis
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  3. Suresh Manandhar
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Chalmers University of Technology, 41296, Göteborg, Sweden

    Bengt Nordström  & Aarne Ranta  & 

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Korkontzelos, I., Klapaftis, I.P., Manandhar, S. (2008). Reviewing and Evaluating Automatic Term Recognition Techniques. In: Nordström, B., Ranta, A. (eds) Advances in Natural Language Processing. GoTAL 2008. Lecture Notes in Computer Science(), vol 5221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85287-2_24

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  • DOI: https://doi.org/10.1007/978-3-540-85287-2_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85286-5

  • Online ISBN: 978-3-540-85287-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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