Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings

Hong-You Chen, Cheng-Syuan Lee, Keng-Te Liao, Shou-De Lin


Abstract
Lexicon relation extraction given distributional representation of words is an important topic in NLP. We observe that the state-of-the-art projection-based methods cannot be generalized to handle unseen hypernyms. We propose to analyze it in the perspective of pollution and construct the corresponding indicator to measure it. We propose a word relation autoencoder (WRAE) model to address the challenge. Experiments on several hypernym-like lexicon datasets show that our model outperforms the competitors significantly.
Anthology ID:
D18-1519
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4834–4839
Language:
URL:
https://aclanthology.org/D18-1519
DOI:
10.18653/v1/D18-1519
Bibkey:
Cite (ACL):
Hong-You Chen, Cheng-Syuan Lee, Keng-Te Liao, and Shou-De Lin. 2018. Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4834–4839, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Word Relation Autoencoder for Unseen Hypernym Extraction Using Word Embeddings (Chen et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1519.pdf