Network Features Based Co-hyponymy Detection
This addresses a specific lexical relation detection task in NLP, offering a novel approach for an area with limited prior work.
The paper tackles the under-investigated problem of co-hyponymy detection in NLP by proposing a supervised model using network measures, achieving high accuracy that matches or exceeds state-of-the-art models.
Distinguishing lexical relations has been a long term pursuit in natural language processing (NLP) domain. Recently, in order to detect lexical relations like hypernymy, meronymy, co-hyponymy etc., distributional semantic models are being used extensively in some form or the other. Even though a lot of efforts have been made for detecting hypernymy relation, the problem of co-hyponymy detection has been rarely investigated. In this paper, we are proposing a novel supervised model where various network measures have been utilized to identify co-hyponymy relation with high accuracy performing better or at par with the state-of-the-art models.