A Study on the Interplay Between the Corpus Size and Parameters of a Distributional Model for Term Classification

Behrang QasemiZadeh


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
Venues:
CompuTerm | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
62–72
Language:
URL:
https://www.aclweb.org/anthology/W16-4708
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W16-4708.pdf