CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction
Yequan Wang, Xiang Li, Aixin Sun, Xuying Meng, Huaming Liao, Jiafeng Guo
Abstract
Quotation extraction aims to extract quotations from written text. There are three components in a quotation: source refers to the holder of the quotation, cue is the trigger word(s), and content is the main body. Existing solutions for quotation extraction mainly utilize rule-based approaches and sequence labeling models. While rule-based approaches often lead to low recalls, sequence labeling models cannot well handle quotations with complicated structures. In this paper, we propose the Context and Former-Label Enhanced Net () for quotation extraction. is able to extract complicated quotations with components of variable lengths and complicated structures. On two public datasets (and ) and one proprietary dataset (), we show that our achieves state-of-the-art performance on complicated quotation extraction.- Anthology ID:
- 2022.coling-1.215
- 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:
- 2438–2449
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.215
- DOI:
- Bibkey:
- Cite (ACL):
- Yequan Wang, Xiang Li, Aixin Sun, Xuying Meng, Huaming Liao, and Jiafeng Guo. 2022. CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2438–2449, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction (Wang et al., COLING 2022)
- Copy Citation:
- PDF:
- https://aclanthology.org/2022.coling-1.215.pdf
- Code
- cofe-ai/CofeNet
Export citation
@inproceedings{wang-etal-2022-cofenet,
title = "{C}ofe{N}et: Context and Former-Label Enhanced Net for Complicated Quotation Extraction",
author = "Wang, Yequan and
Li, Xiang and
Sun, Aixin and
Meng, Xuying and
Liao, Huaming and
Guo, Jiafeng",
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-1.215",
pages = "2438--2449",
abstract = "Quotation extraction aims to extract quotations from written text. There are three components in a quotation: \textit{source} refers to the holder of the quotation, \textit{cue} is the trigger word(s), and \textit{content} is the main body. Existing solutions for quotation extraction mainly utilize rule-based approaches and sequence labeling models. While rule-based approaches often lead to low recalls, sequence labeling models cannot well handle quotations with complicated structures. In this paper, we propose the \textbf{Co}ntext and \textbf{F}ormer-Label \textbf{E}nhanced \textbf{Net} () for quotation extraction. is able to extract complicated quotations with components of variable lengths and complicated structures. On two public datasets (and ) and one proprietary dataset (), we show that our achieves state-of-the-art performance on complicated quotation extraction.",
}
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%0 Conference Proceedings %T CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction %A Wang, Yequan %A Li, Xiang %A Sun, Aixin %A Meng, Xuying %A Liao, Huaming %A Guo, Jiafeng %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 wang-etal-2022-cofenet %X Quotation extraction aims to extract quotations from written text. There are three components in a quotation: source refers to the holder of the quotation, cue is the trigger word(s), and content is the main body. Existing solutions for quotation extraction mainly utilize rule-based approaches and sequence labeling models. While rule-based approaches often lead to low recalls, sequence labeling models cannot well handle quotations with complicated structures. In this paper, we propose the Context and Former-Label Enhanced Net () for quotation extraction. is able to extract complicated quotations with components of variable lengths and complicated structures. On two public datasets (and ) and one proprietary dataset (), we show that our achieves state-of-the-art performance on complicated quotation extraction. %U https://aclanthology.org/2022.coling-1.215 %P 2438-2449
Markdown (Informal)
[CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction](https://aclanthology.org/2022.coling-1.215) (Wang et al., COLING 2022)
- CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction (Wang et al., COLING 2022)
ACL
- Yequan Wang, Xiang Li, Aixin Sun, Xuying Meng, Huaming Liao, and Jiafeng Guo. 2022. CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2438–2449, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.