Proceedings

Learning Term Embeddings for Hypernymy Identification

Zheng Yu, Haixun Wang, Xuemin Lin, Min Wang

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Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 1390-1396.

Abstract

Hypernymy identification aims at detecting if isA relationship holds between two words or phrases. Most previous methods are based on lexical patterns or the Distributional Inclusion Hypothesis, and the accuracy of such methods is not ideal. In this paper, we propose a simple yet effective supervision framework to identify hypernymy relations using distributed term representations (a.k.a term embeddings). First, we design a distance-margin neural network to learn term embeddings based on some pre-extracted hypernymy data. Then, we apply such embeddings as term features to identify positive hypernymy pairs through a supervision method. Experimental results demonstrate that our approach outperforms other supervised methods on two popular datasets and the learned term embeddings has better quality than existing term distributed representations with respect to hypernymy identification.

Zheng Yu, Haixun Wang, Xuemin Lin, and Min Wang. 2015. Learning Term Embeddings for Hypernymy Identification. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 1390–1396. https://ijcai.org/proceedings/2015/200
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