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        <title>A Study on the Interplay Between the Corpus Size and Parameters of a Distributional Model for Term Classification</title>
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        <namePart type="given">Behrang</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T A Study on the Interplay Between the Corpus Size and Parameters of a Distributional Model for Term Classification
%A QasemiZadeh, Behrang
%Y Drouin, Patrick
%Y Grabar, Natalia
%Y Hamon, Thierry
%Y Kageura, Kyo
%Y Takeuchi, Koichi
%S Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F qasemizadeh-2016-study
%X 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.
%U https://aclanthology.org/W16-4708
%P 62-72

Markdown (Informal)

[A Study on the Interplay Between the Corpus Size and Parameters of a Distributional Model for Term Classification](https://aclanthology.org/W16-4708) (QasemiZadeh, CompuTerm 2016)

• A Study on the Interplay Between the Corpus Size and Parameters of a Distributional Model for Term Classification (QasemiZadeh, CompuTerm 2016)

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.