Improving term candidates selection using terminological tokens

Author:

Vàzquez Mercè1,Oliver Antoni1

Affiliation:

1. Universitat Oberta de Catalunya

Abstract

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.

Publisher

John Benjamins Publishing Company

Subject

Library and Information Sciences,Communication,Language and Linguistics

Reference67 articles.

1. A Computational Linguistic Approach to Automatic Term Recognition;Ananiadou,1994

2. A methodology for automatic term recognition

3. Term Extraction from Unrestricted Text;Arppe,1995

4. Improving Term Extraction with Terminological Resources

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