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
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
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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)
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PDF:
https://aclanthology.org/2022.coling-1.215.pdf