A Domain Independent Approach for Extracting Terms from Research Papers

Abstract

We study the problem of extracting terms from research papers, which is an important step towards building knowledge graphs in research domain. Existing terminology extraction approaches are mostly domain dependent. They use domain specific linguistic rules, supervised machine learning techniques or a combination of the two to extract the terms. Using domain knowledge requires much human effort, e.g., manually composing a set of linguistic rules or labeling a large corpus, and hence limits the applicability of the existing approaches. To overcome this limitation, we propose a new terminology extraction approach that makes use of no knowledge from any specific domain. In particular, we use the title words and the keywords in research papers as the seeding terms and word2vec to identify similar terms from an open-domain corpus as the candidate terms, which are then filtered by checking their occurrence in the research papers. We repeat this process using the newly found terms until no new candidate term can be found. We conduct extensive experiments on the proposed approach. The results show that our approach can extract the terms effectively, while being domain independent.

Birong Jiang—This work is done when Birong is a visiting student at the University of Melbourne.