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Quotation extraction aims to extract quotations from written text. There arethree 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 solutionsfor quotation extraction mainly utilize rule-based approaches and sequencelabeling models. While rule-based approaches often lead to low recalls,sequence labeling models cannot well handle quotations with complicatedstructures. In this paper, we propose the Context and Former-Label Enhanced Net(CofeNet) for quotation extraction. CofeNet is able to extract complicatedquotations with components of variable lengths and complicated structures. Ontwo public datasets (i.e., PolNeAR and Riqua) and one proprietary dataset(i.e., PoliticsZH), we show that our CofeNet achieves state-of-the-artperformance on complicated quotation extraction.

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