Related papers: When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models

When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models

URL: http://arxiv.org/abs/2010.04941v1
Date: Sat, 10 Oct 2020 08:34:19 GMT
Title: When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models
Authors: Changlong Yu, Jialong Han, Peifeng Wang, Yangqiu Song, Hongming Zhang, Wilfred Ng, Shuming Shi
Abstract summary: This paper addresses whether an is-a relationship exists between words (x, y) with the help of large textual corpora. 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. 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.
Score: 59.46552488974247
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
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 achieves competitive improvements and the case study shows its better interpretability.

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