Distant Supervised Relation Extraction with Separate Head-Tail CNN

Rui Xing, Jie Luo


Abstract
Distant supervised relation extraction is an efficient and effective strategy to find relations between entities in texts. However, it inevitably suffers from mislabeling problem and the noisy data will hinder the performance. In this paper, we propose the Separate Head-Tail Convolution Neural Network (SHTCNN), a novel neural relation extraction framework to alleviate this issue. In this method, we apply separate convolution and pooling to the head and tail entity respectively for extracting better semantic features of sentences, and coarse-to-fine strategy to filter out instances which do not have actual relations in order to alleviate noisy data issues. Experiments on a widely used dataset show that our model achieves significant and consistent improvements in relation extraction compared to statistical and vanilla CNN-based methods.
Anthology ID:
D19-5533
Volume:
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | WNUT | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
249–258
Language:
URL:
https://www.aclweb.org/anthology/D19-5533
DOI:
10.18653/v1/D19-5533
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-5533.pdf