Using Distributional Thesaurus Embedding for Co-hyponymy Detection

Abhik Jana, Nikhil Reddy Varimalla, Pawan Goyal


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.
Anthology ID:
2020.lrec-1.707
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
5766–5771
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.707
DOI:
Bibkey:
Cite (ACL):
Abhik Jana, Nikhil Reddy Varimalla, and Pawan Goyal. 2020. Using Distributional Thesaurus Embedding for Co-hyponymy Detection. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 5766–5771, Marseille, France. European Language Resources Association.
Cite (Informal):
Using Distributional Thesaurus Embedding for Co-hyponymy Detection (Jana et al., LREC 2020)
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PDF:
https://aclanthology.org/2020.lrec-1.707.pdf