MFF-CNER: A Multi-feature Fusion Model for Chinese Named Entity Recognition in Finance Securities

Abstract

The objective of Chinese financial securities named entity recognition is to extract relevant entities from unstructured Chinese text, such as news, announcements, and research reports, that impact security prices. Recognizing entities in this field is challenging due to the abundance of specialized terms, diverse expressions, and the limited feature extraction capabilities of traditional models. To address this, we propose MFF-CNER, a multi-feature fusion model, to improve the effectiveness of Chinese financial securities named entity recognition. MFF-CNER encompasses several key steps. Firstly, it leverages a BERT pre-training model to capture semantic features at the character level. Secondly, a BiLSTM network is utilized to capture contextual features specific to financial securities text. Additionally, we introduce an Iterated Dilated Convolutional Neural Network (IDCNN) to blend, and extract local features, incorporating an Attention mechanism for weighted feature integration. Finally, the predicted sequences are optimized, and decoded using the Conditional Random Field (CRF). To validate the state-of-the-art performance of MFF-CNER in this domain, we compare it with five popular methods on a Chinese financial securities dataset annotated with the BIO labeling scheme. Notably, MFF-CNER demonstrates superior performance while maintaining compatibility among its components. Furthermore, we evaluate the applicability of MFF-CNER in the Chinese financial securities domain by utilizing public datasets from diverse domains, including social media (WEIBO), and news (MSRA). This research holds practical significance for downstream applications, such as constructing financial securities knowledge graphs, and analyzing factors that influence security prices.