Exploration of a rich feature set for automatic term extraction

Abstract

Despite the importance of the term extraction methods and that several efforts have been devoted to improve them, they still have 4 main problems: (i) noise and silence generation; (ii) difficulty dealing with high number of terms; (iii) human effort and time to evaluate the terms; and (iv) still limited extraction results. In this paper, we deal with these four major problems in automatic term extraction by exploring a rich feature set in a machine learning approach. We minimized these problems and achieved state of the art results for unigrams in Brazilian Portuguese. © Springer-Verlag 2013.