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

The architecture of CofeNet


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 (CofeNet) for quotation extraction. CofeNet is able to extract complicated quotations with components of variable lengths and complicated structures. On two public datasets and one proprietary dataset, we show that our achieves state-of-the-art performance on complicated quotation extraction.

In Proceedings of the 29th International Conference on Computational Linguistics
Yequan Wang
Yequan Wang
Researcher, Team Leader

My research interests include large model, emboddied AI and NLP.