Investigating Context Parameters in Technology Term Recognition
Behrang QasemiZadeh, Siegfried Handschuh · 2014
We propose and evaluate the task of technology term recognition: a method to extract technology terms at a synchronic level from a corpus of scientific publications.The proposed method is built on the principles of terminology extraction and distributional semantics.It is realized as a regression task in a vector space model.In this method, candidate terms are first extracted from text.Subsequently, using the random indexing technique, the extracted candidate terms are represented as vectors in a Euclidean vector space of reduced dimensionality.These vectors are derived from the frequency of co-occurrences of candidate terms and words in windows of text surrounding candidate terms in the input corpus (context window).The constructed vector space and a set of manually tagged technology terms (reference vectors) in a k-nearest neighbours regression framework is then used to identify terms that signify technology concepts.We examine a number of factors that play roles in the performance of the proposed method, i.e. the configuration of context windows, neighborhood size (k) selection, and reference vector size.