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.