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http://hdl.handle.net/10609/80325
Title: Improving term candidates selection using terminological tokens
Author: Vàzquez Garcia, Mercè  
Oliver González, Antoni
Keywords: automatic term extraction
terminological tokens
TSR filtering method
terminology extraction
domain-specific corpora
terminological units
TBXTools
term candidates
Issue Date: 11-Jun-2018
Publisher: Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication
Citation: Vàzquez, M.; Oliver, A. (2018). "Improving term candidates selection using terminological tokens". Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication, p. 122-147. ISSN 0929-9971. DOI: 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.
Language: English
URI: http://hdl.handle.net/10609/80325
ISSN: 0929-9971
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