Learning Fine-grained Relations from Chinese User Generated Categories

Chengyu Wang, Yan Fan, Xiaofeng He, Aoying Zhou


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.
Anthology ID:
D17-1273
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2577–2587
Language:
URL:
https://www.aclweb.org/anthology/D17-1273
DOI:
10.18653/v1/D17-1273
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1273.pdf