@inproceedings{yu-etal-2020-hearst,
title = "When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models",
author = "Yu, Changlong and
Han, Jialong and
Wang, Peifeng and
Song, Yangqiu and
Zhang, Hongming and
Ng, Wilfred and
Shi, Shuming",
editor = "Webber, Bonnie and
Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.emnlp-main.502/",
doi = "10.18653/v1/2020.emnlp-main.502",
pages = "6208--6217",
abstract = "We address hypernymy detection, i.e., whether an is-a relationship exists between words (x ,y), with the help of large textual corpora. Most conventional approaches to this task have been categorized to be either pattern-based or distributional. Recent studies suggest that pattern-based ones are superior, if large-scale Hearst pairs are extracted and fed, with the sparsity of unseen (x ,y) pairs relieved. However, they become invalid in some specific sparsity cases, where x or y is not involved in any pattern. For the first time, this paper quantifies the non-negligible existence of those specific cases. We also demonstrate that distributional methods are ideal to make up for pattern-based ones in such cases. We devise a complementary framework, under which a pattern-based and a distributional model collaborate seamlessly in cases which they each prefer. On several benchmark datasets, our framework demonstrates improvements that are both competitive and explainable."
}
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%0 Conference Proceedings
%T When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models
%A Yu, Changlong
%A Han, Jialong
%A Wang, Peifeng
%A Song, Yangqiu
%A Zhang, Hongming
%A Ng, Wilfred
%A Shi, Shuming
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F yu-etal-2020-hearst
%X We address hypernymy detection, i.e., whether an is-a relationship exists between words (x ,y), with the help of large textual corpora. Most conventional approaches to this task have been categorized to be either pattern-based or distributional. Recent studies suggest that pattern-based ones are superior, if large-scale Hearst pairs are extracted and fed, with the sparsity of unseen (x ,y) pairs relieved. However, they become invalid in some specific sparsity cases, where x or y is not involved in any pattern. For the first time, this paper quantifies the non-negligible existence of those specific cases. We also demonstrate that distributional methods are ideal to make up for pattern-based ones in such cases. We devise a complementary framework, under which a pattern-based and a distributional model collaborate seamlessly in cases which they each prefer. On several benchmark datasets, our framework demonstrates improvements that are both competitive and explainable.
%R 10.18653/v1/2020.emnlp-main.502
%U https://aclanthology.org/2020.emnlp-main.502/
%U https://doi.org/10.18653/v1/2020.emnlp-main.502
%P 6208-6217
Markdown (Informal)
[When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models](https://aclanthology.org/2020.emnlp-main.502/) (Yu et al., EMNLP 2020)
ACL