计算机技术与发展2025，Vol.35Issue(3)：125-132,8.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0336
基于LERT和BiTCN的金融领域命名实体识别
Named Entity Recognition in Finance Field Based on LERT and BiTCN
摘要
Abstract
In order to solve the problem that the traditional named entity recognition method is difficult to solve the problem of multiple meanings of words in financial texts and insufficient semantic feature extraction of texts,a named entity recognition model in the financial field based on LERT-BiTCN-CRF was proposed.Firstly,the LERT model was used to pre-train the input financial text to generate the corresponding character vectors.Then,by adding a reverse convolutional layer inside the TCN,it was improved into BiTCN,and the BiTCN was used to encode the character vector to extract the global semantic features of the character vector.Finally,CRF was used to decode to obtain the best predicted label sequence.Comparative experiments were carried out on two financial domain datasets,the public dataset ChFinAnnandthe self-made dataset FinanceNER,and the F1 values of the model on the two datasets reached84.16％and 92.17％,respectively. Compared with other models,the proposed model has better effect in the named entity recognition task in the financial field,indicating that the model has certain effectiveness.At the same time,comparative experiments were carried out on the public Resume dataset,and the F1 value of the model was increased by 2.31％ compared with the baseline model BiGRU-CRF,indicating that the model has a certain generalization.关键词
LERT模型/金融领域/命名实体识别/双向时间卷积网络/条件随机场Key words
LERT model/financial field/named entity recognition/bi-directional temporal convolutional network(BiTCN)/conditional random field(CRF)分类
计算机与自动化引用本文复制引用
陈雪松,王璐瑶,王浩畅..基于LERT和BiTCN的金融领域命名实体识别[J].计算机技术与发展,2025,35(3):125-132,8.基金项目
国家自然科学基金资助项目(61402099,61702093) （61402099,61702093）
