@inproceedings{qasemizadeh-2016-study,
    title = "A Study on the Interplay Between the Corpus Size and Parameters of a Distributional Model for Term Classification",
    author = "QasemiZadeh, Behrang",
    editor = "Drouin, Patrick  and
      Grabar, Natalia  and
      Hamon, Thierry  and
      Kageura, Kyo  and
      Takeuchi, Koichi",
    booktitle = "Proceedings of the 5th International Workshop on Computational Terminology (Computerm2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/W16-4708/",
    pages = "62--72",
    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."
}