Noise Mitigation for Neural Entity Typing and Relation Extraction

Yadollah Yaghoobzadeh, Heike Adel, Hinrich Schütze


Abstract
In this paper, we address two different types of noise in information extraction models: noise from distant supervision and noise from pipeline input features. Our target tasks are entity typing and relation extraction. For the first noise type, we introduce multi-instance multi-label learning algorithms using neural network models, and apply them to fine-grained entity typing for the first time. Our model outperforms the state-of-the-art supervised approach which uses global embeddings of entities. For the second noise type, we propose ways to improve the integration of noisy entity type predictions into relation extraction. Our experiments show that probabilistic predictions are more robust than discrete predictions and that joint training of the two tasks performs best.
Anthology ID:
E17-1111
Volume:
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
Month:
April
Year:
2017
Address:
Valencia, Spain
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1183–1194
Language:
URL:
https://www.aclweb.org/anthology/E17-1111
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/E17-1111.pdf