@inproceedings{bai-ritter-2019-structured,
title = "{S}tructured {M}inimally {S}upervised {L}earning for {N}eural {R}elation {E}xtraction",
author = "Bai, Fan and
Ritter, Alan",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1310",
doi = "10.18653/v1/N19-1310",
pages = "3057--3069",
abstract = "We present an approach to minimally supervised relation extraction that combines the benefits of learned representations and structured learning, and accurately predicts sentence-level relation mentions given only proposition-level supervision from a KB. By explicitly reasoning about missing data during learning, our approach enables large-scale training of 1D convolutional neural networks while mitigating the issue of label noise inherent in distant supervision. Our approach achieves state-of-the-art results on minimally supervised sentential relation extraction, outperforming a number of baselines, including a competitive approach that uses the attention layer of a purely neural model.",
}
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%0 Conference Proceedings
%T Structured Minimally Supervised Learning for Neural Relation Extraction
%A Bai, Fan
%A Ritter, Alan
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F bai-ritter-2019-structured
%X We present an approach to minimally supervised relation extraction that combines the benefits of learned representations and structured learning, and accurately predicts sentence-level relation mentions given only proposition-level supervision from a KB. By explicitly reasoning about missing data during learning, our approach enables large-scale training of 1D convolutional neural networks while mitigating the issue of label noise inherent in distant supervision. Our approach achieves state-of-the-art results on minimally supervised sentential relation extraction, outperforming a number of baselines, including a competitive approach that uses the attention layer of a purely neural model.
%R 10.18653/v1/N19-1310
%U https://aclanthology.org/N19-1310
%U https://doi.org/10.18653/v1/N19-1310
%P 3057-3069
Markdown (Informal)
[Structured Minimally Supervised Learning for Neural Relation Extraction](https://aclanthology.org/N19-1310) (Bai & Ritter, NAACL 2019)
ACL
- Fan Bai and Alan Ritter. 2019. Structured Minimally Supervised Learning for Neural Relation Extraction. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 3057–3069, Minneapolis, Minnesota. Association for Computational Linguistics.