## CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction

##### 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
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2438–2449
Language:
URL:
https://aclanthology.org/2022.coling-1.215
DOI:
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)
PDF:
https://aclanthology.org/2022.coling-1.215.pdf