CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction
This addresses quotation extraction for NLP applications, offering improved handling of complex structures, though it appears incremental as it builds on sequence labeling models.
The paper tackled the problem of extracting quotations with complicated structures from text, proposing CofeNet, which achieved state-of-the-art performance on three datasets including PolNeAR, Riqua, and PoliticsZH.
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 (i.e., PolNeAR and Riqua) and one proprietary dataset (i.e., PoliticsZH), we show that our CofeNet achieves state-of-the-art performance on complicated quotation extraction.