计算机工程与应用2024，Vol.60Issue(14)：123-132,10.DOI:10.3778/j.issn.1002-8331.2304-0224
基于Fin-BERT的中文金融领域事件抽取方法
Fin-BERT-Based Event Extraction Method for Chinese Financial Domain
李熠 1耿朝阳 1杨丹1
作者信息
- 1. 西安工业大学计算机科学与工程学院,西安 710021
- 折叠
摘要
Abstract
Event extraction aims to extract human-interest information from massive amounts of unstructured text.Cur-rently,most existing event extraction methods are based on general corpora and rarely consider domain-specific prior knowledge.Moreover,most methods cannot handle well the case where multiple events exist in the same document,and they perform poorly when faced with a large number of negative examples.To address these issues,this paper proposes a model called Fin-PTPCG based on Fin-BERT(financial bidirectional encoder representation from Transformers)and PTPCG(pseudo-trigger-aware pruned complete graph).This method fully utilizes the expression ability of the Fin-BERT pre-training model and incorporates domain-specific prior knowledge during the encoding stage.In the event detection module,multiple binary classifiers are stacked to ensure that the model can effectively identify the situation of multiple events in a document and screen out negative examples.Combined with the decoding module of the PTPCG model,entities are extracted and connected into a complete graph and pruned by calculating a similarity matrix.The problem of unlabeled triggers is solved by selecting pseudo-triggers.Finally,the event extraction is achieved by the event classifier.This method achieves a 0.7 and 3.7 percentage points improvement in F1 score compared to the baselines on the ChFinAnn and Duee-fin datasets for the event extraction task.关键词
事件抽取/事件检测/信息抽取/自然语言处理Key words
event extraction/event detection/information extraction/natural language processing分类
信息技术与安全科学引用本文复制引用
李熠,耿朝阳,杨丹..基于Fin-BERT的中文金融领域事件抽取方法[J].计算机工程与应用,2024,60(14):123-132,10.
