Document-Level Event Subject Pair Recognition

摘要：

　　In recent years,financial events in the stock market have increased dramatically.Extracting valuable information automatically from massive finan-cial documents can provide effective support for the analysis of financial events.This paper just proposes an end-to-end document-level subject pair recognition method.It aims to recognize the subject pair,i.e.the subject and the object of an event.Given one document and the predefined event type set,this method will output all the corresponding subject pairs related to each event type.Subject pair recognition is certainly a document-level extraction task since it needs to scan the entire document to output desired subject pairs.This paper constructs a global document-level vector based on sentence-level vectors which are encoded from BERT.The global document-level vector aims to cover the information carried by the entire document.It is utilized to guide the extraction process conducted sentence by sentence.After considering global information,our method obtains superior experimental results.

关键词： Subject pair recognition Document-level encoding Event type

作者: Zhenyu Hu Ming Liu Yin Wu Jiexin Xu Bing Qin JinLong Li

作者单位: Harbin Institute of Technology,Harbin,China Harbin Institute of Technology,Harbin,China;PENG CHENG Laboratory,Shenzhen,China China Merchants Bank,Shenzhen,China

会议类型: 国际会议

会议名称: 9th CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC 2020)

会议地点: 郑州

会议语种:英文

页码: 283-293

在线出版日期: 2020-10-14（万方平台首次上网日期，不代表论文的发表时间）