@inproceedings{jana-etal-2020-using,
title = "Using Distributional Thesaurus Embedding for Co-hyponymy Detection",
author = "Jana, Abhik and
Varimalla, Nikhil Reddy and
Goyal, Pawan",
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.707",
pages = "5766--5771",
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.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Using Distributional Thesaurus Embedding for Co-hyponymy Detection
%A Jana, Abhik
%A Varimalla, Nikhil Reddy
%A Goyal, Pawan
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F jana-etal-2020-using
%X 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.
%U https://aclanthology.org/2020.lrec-1.707
%P 5766-5771
Markdown (Informal)
[Using Distributional Thesaurus Embedding for Co-hyponymy Detection](https://aclanthology.org/2020.lrec-1.707) (Jana et al., LREC 2020)
ACL