Using Distributional Thesaurus Embedding for Co-hyponymy Detection
Computation and Language
2020-02-27 v1
Abstract
Discriminating lexical relations among distributionally similar words has always been a challenge for natural language processing (NLP) community. In this paper, we investigate whether the network embedding of distributional thesaurus can be effectively utilized to detect co-hyponymy relations. By extensive experiments over three benchmark datasets, we show that the vector representation obtained by applying node2vec on distributional thesaurus outperforms the state-of-the-art models for binary classification of co-hyponymy vs. hypernymy, as well as co-hyponymy vs. meronymy, by huge margins.
Cite
@article{arxiv.2002.11506,
title = {Using Distributional Thesaurus Embedding for Co-hyponymy Detection},
author = {Abhik Jana and Nikhil Reddy Varimalla and Pawan Goyal},
journal= {arXiv preprint arXiv:2002.11506},
year = {2020}
}
Comments
Accepted in LREC 2020. arXiv admin note: text overlap with arXiv:1802.04609