Abstract
We propose and evaluate a method for identifying co-hyponym lexical units in a terminological resource. The principles of term recognition and distributional semantics are combined to extract terms from a similar category of concept. Given a set of candidate terms, random projections are employed to represent them as low-dimensional vectors. These vectors are derived automatically from the frequency of the co-occurrences of the candidate terms and words that appear within windows of text in their proximity (context-windows). In a k-nearest neighbours framework, these vectors are classified using a small set of manually annotated terms which exemplify concept categories. We then investigate the interplay between the size of the corpus that is used for collecting the co-occurrences and a number of factors that play roles in the performance of the proposed method: the configuration of context-windows for collecting co-occurrences, the selection of neighbourhood size (k), and the choice of similarity metric.- Anthology ID:
- W16-4708
- Volume:
- Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Venue:
- CompuTerm
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 62–72
- Language:
- URL:
- https://aclanthology.org/W16-4708
- DOI:
- Cite (ACL):
- Behrang QasemiZadeh. 2016. A Study on the Interplay Between the Corpus Size and Parameters of a Distributional Model for Term Classification. In Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016), pages 62–72, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- A Study on the Interplay Between the Corpus Size and Parameters of a Distributional Model for Term Classification (QasemiZadeh, CompuTerm 2016)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W16-4708.pdf