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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.- Anthology ID:
- 2022.coling-1.234
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 2651–2657
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.234/
- DOI:
- Bibkey:
- Cite (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.
- Cite (Informal):
- A Simple Model for Distantly Supervised Relation Extraction (Rao et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.234.pdf
Export citation
@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",
editor = "Calzolari, Nicoletta and
Huang, Chu-Ren and
Kim, Hansaem and
Pustejovsky, James and
Wanner, Leo and
Choi, Key-Sun and
Ryu, Pum-Mo and
Chen, Hsin-Hsi and
Donatelli, Lucia and
Ji, Heng and
Kurohashi, Sadao and
Paggio, Patrizia and
Xue, Nianwen and
Kim, Seokhwan and
Hahm, Younggyun and
He, Zhong and
Lee, Tony Kyungil and
Santus, Enrico and
Bond, Francis and
Na, Seung-Hoon",
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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%0 Conference Proceedings %T A Simple Model for Distantly Supervised Relation Extraction %A Rao, Ziqin %A Feng, Fangxiang %A Li, Ruifan %A Wang, Xiaojie %Y Calzolari, Nicoletta %Y Huang, Chu-Ren %Y Kim, Hansaem %Y Pustejovsky, James %Y Wanner, Leo %Y Choi, Key-Sun %Y Ryu, Pum-Mo %Y Chen, Hsin-Hsi %Y Donatelli, Lucia %Y Ji, Heng %Y Kurohashi, Sadao %Y Paggio, Patrizia %Y Xue, Nianwen %Y Kim, Seokhwan %Y Hahm, Younggyun %Y He, Zhong %Y Lee, Tony Kyungil %Y Santus, Enrico %Y Bond, Francis %Y Na, Seung-Hoon %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)
- A Simple Model for Distantly Supervised Relation Extraction (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.