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        <title>Learning Fine-grained Relations from Chinese User Generated Categories</title>
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    <abstract>User generated categories (UGCs) are short texts that reflect how people describe and organize entities, expressing rich semantic relations implicitly. While most methods on UGC relation extraction are based on pattern matching in English circumstances, learning relations from Chinese UGCs poses different challenges due to the flexibility of expressions. In this paper, we present a weakly supervised learning framework to harvest relations from Chinese UGCs. We identify is-a relations via word embedding based projection and inference, extract non-taxonomic relations and their category patterns by graph mining. We conduct experiments on Chinese Wikipedia and achieve high accuracy, outperforming state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T Learning Fine-grained Relations from Chinese User Generated Categories
%A Wang, Chengyu
%A Fan, Yan
%A He, Xiaofeng
%A Zhou, Aoying
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F wang-etal-2017-learning-fine
%X User generated categories (UGCs) are short texts that reflect how people describe and organize entities, expressing rich semantic relations implicitly. While most methods on UGC relation extraction are based on pattern matching in English circumstances, learning relations from Chinese UGCs poses different challenges due to the flexibility of expressions. In this paper, we present a weakly supervised learning framework to harvest relations from Chinese UGCs. We identify is-a relations via word embedding based projection and inference, extract non-taxonomic relations and their category patterns by graph mining. We conduct experiments on Chinese Wikipedia and achieve high accuracy, outperforming state-of-the-art methods.
%R 10.18653/v1/D17-1273
%U https://aclanthology.org/D17-1273
%U https://doi.org/10.18653/v1/D17-1273
%P 2577-2587

Markdown (Informal)

[Learning Fine-grained Relations from Chinese User Generated Categories](https://aclanthology.org/D17-1273) (Wang et al., EMNLP 2017)

• Learning Fine-grained Relations from Chinese User Generated Categories (Wang et al., EMNLP 2017)

ACL

• Chengyu Wang, Yan Fan, Xiaofeng He, and Aoying Zhou. 2017. Learning Fine-grained Relations from Chinese User Generated Categories. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2577–2587, Copenhagen, Denmark. Association for Computational Linguistics.