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arXiv · 2002.11506

Using Distributional Thesaurus Embedding for Co-hyponymy Detection

Abhik Jana·Nikhil Reddy Varimalla·Pawan Goyal
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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.

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Abhik Jana, Nikhil Reddy Varimalla, Pawan Goyal. 2020-02-24. Using Distributional Thesaurus Embedding for Co-hyponymy Detection. https://arxiv.org/abs/2002.11506

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