@inproceedings{rao-etal-2022-simple,
title = "A Simple Model for Distantly Supervised Relation Extraction",
author = "Rao, Ziqin and
Feng, Fangxiang and
Li, Ruifan and
Wang, Xiaojie",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.234",
pages = "2651--2657",
abstract = "Distantly supervised relation extraction is challenging due to the noise within data. Recent methods focus on exploiting bag representations based on deep neural networks with complex de-noising scheme to achieve remarkable performance. In this paper, we propose a simple but effective \textbf{B}ERT-based \textbf{G}raph convolutional network \textbf{M}odel (i.e., BGM). Our BGM comprises of an instance embedding module and a bag representation module. The instance embedding module uses a BERT-based pretrained language model to extract key information from each instance. The bag representaion module constructs the corresponding bag graph then apply a convolutional operation to obtain the bag representation. Our BGM model achieves a considerable improvement on two benchmark datasets, i.e., NYT10 and GDS.",
}
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<abstract>Distantly supervised relation extraction is challenging due to the noise within data. Recent methods focus on exploiting bag representations based on deep neural networks with complex de-noising scheme to achieve remarkable performance. In this paper, we propose a simple but effective BERT-based Graph convolutional network Model (i.e., BGM). Our BGM comprises of an instance embedding module and a bag representation module. The instance embedding module uses a BERT-based pretrained language model to extract key information from each instance. The bag representaion module constructs the corresponding bag graph then apply a convolutional operation to obtain the bag representation. Our BGM model achieves a considerable improvement on two benchmark datasets, i.e., NYT10 and GDS.</abstract>
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%0 Conference Proceedings
%T A Simple Model for Distantly Supervised Relation Extraction
%A Rao, Ziqin
%A Feng, Fangxiang
%A Li, Ruifan
%A Wang, Xiaojie
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F rao-etal-2022-simple
%X Distantly supervised relation extraction is challenging due to the noise within data. Recent methods focus on exploiting bag representations based on deep neural networks with complex de-noising scheme to achieve remarkable performance. In this paper, we propose a simple but effective BERT-based Graph convolutional network Model (i.e., BGM). Our BGM comprises of an instance embedding module and a bag representation module. The instance embedding module uses a BERT-based pretrained language model to extract key information from each instance. The bag representaion module constructs the corresponding bag graph then apply a convolutional operation to obtain the bag representation. Our BGM model achieves a considerable improvement on two benchmark datasets, i.e., NYT10 and GDS.
%U https://aclanthology.org/2022.coling-1.234
%P 2651-2657
Markdown (Informal)
[A Simple Model for Distantly Supervised Relation Extraction](https://aclanthology.org/2022.coling-1.234) (Rao et al., COLING 2022)
ACL
- Ziqin Rao, Fangxiang Feng, Ruifan Li, and Xiaojie Wang. 2022. A Simple Model for Distantly Supervised Relation Extraction. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2651–2657, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.