{"title":"基于多神经网络融合的财务事件主题提取方法","authors":"Zhunqin Wang, Zhiming Liu, Lingyun Luo, Xianglong Chen","doi":"10.1109/AEMCSE50948.2020.00084","DOIUrl":null,"url":null,"abstract":"Event extraction is a fundamental task in the domain of public opinion monitoring and financial risk control. Subject extraction of events with specific types is the kernel of event extraction. At present, there are some problems still existing in the mainstream event subject extraction methods, such as the inadequate use of semantic relationship between Chinese characters and the weak ability of feature learning. In order to solve these problems, this paper introduces the BERT (Bidirectional Encoder Representations from Transformers) pre-training model to enhance the semantic representation of characters, then proposes a novel event subject extraction method combing convolutional neural network (CNN) and long short-term memory (LSTM) to improve the ability of feature learning in the model. Experimental results show that the F1 score of the method proposed in this paper can reach 86.99%, which greatly improves the identification accuracy of the event subject in the financial domain.","PeriodicalId":246841,"journal":{"name":"2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Multi-Neural Network Fusion Based Method for Financial Event Subject Extraction\",\"authors\":\"Zhunqin Wang, Zhiming Liu, Lingyun Luo, Xianglong Chen\",\"doi\":\"10.1109/AEMCSE50948.2020.00084\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Event extraction is a fundamental task in the domain of public opinion monitoring and financial risk control. Subject extraction of events with specific types is the kernel of event extraction. At present, there are some problems still existing in the mainstream event subject extraction methods, such as the inadequate use of semantic relationship between Chinese characters and the weak ability of feature learning. In order to solve these problems, this paper introduces the BERT (Bidirectional Encoder Representations from Transformers) pre-training model to enhance the semantic representation of characters, then proposes a novel event subject extraction method combing convolutional neural network (CNN) and long short-term memory (LSTM) to improve the ability of feature learning in the model. Experimental results show that the F1 score of the method proposed in this paper can reach 86.99%, which greatly improves the identification accuracy of the event subject in the financial domain.\",\"PeriodicalId\":246841,\"journal\":{\"name\":\"2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)\",\"volume\":\"23 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-04-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AEMCSE50948.2020.00084\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 3rd International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AEMCSE50948.2020.00084","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
事件提取是舆情监测和金融风险控制领域的一项基础性工作。特定类型事件的主题提取是事件提取的核心。目前,主流的事件主语提取方法还存在对汉字语义关系利用不足、特征学习能力弱等问题。为了解决这些问题,本文引入了BERT (Bidirectional Encoder Representations from Transformers)预训练模型来增强字符的语义表示,然后提出了一种结合卷积神经网络(CNN)和长短期记忆(LSTM)的事件主题提取方法,以提高模型的特征学习能力。实验结果表明,本文提出的方法的F1分数可以达到86.99%,大大提高了金融领域事件主体的识别准确率。
A Multi-Neural Network Fusion Based Method for Financial Event Subject Extraction
Event extraction is a fundamental task in the domain of public opinion monitoring and financial risk control. Subject extraction of events with specific types is the kernel of event extraction. At present, there are some problems still existing in the mainstream event subject extraction methods, such as the inadequate use of semantic relationship between Chinese characters and the weak ability of feature learning. In order to solve these problems, this paper introduces the BERT (Bidirectional Encoder Representations from Transformers) pre-training model to enhance the semantic representation of characters, then proposes a novel event subject extraction method combing convolutional neural network (CNN) and long short-term memory (LSTM) to improve the ability of feature learning in the model. Experimental results show that the F1 score of the method proposed in this paper can reach 86.99%, which greatly improves the identification accuracy of the event subject in the financial domain.