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Article published In: Computational terminology and filtering of terminological information
Edited by Patrick Drouin, Natalia Grabar, Thierry Hamon, Kyo Kageura and Koichi Takeuchi
[Terminology 24:1] 2018
► pp. 122–147

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Improving term candidates selection using terminological tokens

 | Universitat Oberta de Catalunya
 | Universitat Oberta de Catalunya
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Available under the Creative Commons Attribution-NonCommercial (CC BY-NC) 4.0 license.

For any use beyond this license, please contact the publisher at rights@benjamins.nl.

Published online: 31 May 2018
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https://doi.org/10.1075/term.00016.vaz
Abstract
The identification of reliable terms from domain-specific corpora using computational methods is a task that has to be validated manually by specialists, which is a highly time-consuming activity. To reduce this effort and improve term candidate selection, we implemented the Token Slot Recognition method, a filtering method based on terminological tokens which is used to rank extracted term candidates from domain-specific corpora. This paper presents the implementation of the term candidates filtering method we developed in linguistic and statistical approaches applied for automatic term extraction using several domain-specific corpora in different languages. We observed that the filtering method outperforms term candidate selection by ranking a higher number of terms at the top of the term candidate list than raw frequency, and for statistical term extraction the improvement is between 15% and 25% both in precision and recall. Our analyses further revealed a reduction in the number of term candidates to be validated manually by specialists. In conclusion, the number of term candidates extracted automatically from domain-specific corpora has been reduced significantly using the Token Slot Recognition filtering method, so term candidates can be easily and quickly validated by specialists.
Article outline
  • 1.Introduction
  • 2.Background
  • 3.Materials and methods
  • 4.Results and discussion
    • 4.1Experimental settings
    • 4.2Term extraction procedure
    • 4.3Results and evaluation
      • Results for JRC Economics English
        • Statistical term extraction
        • Linguistic term extraction
      • Results for JRC Economics Spanish
      • Results for JRC Economics French
      • Results for IULA Economics Spanish
      • Results for IULA Health Spanish
      • Results for TERMCAT Social Services Spanish
      • Results for TERMCAT Social Services Catalan
    • 4.4Discussion
  • 5.Conclusions and future work
  • References
References (67)
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