Citation entity recognition method using multi‐feature semantic fusion based on deep learning
Jie Gao, Zuping Zhang, Ping Cao, Wei Hua Huang, Fangfang Li · Concurrency and Computation Practice and Experience · 2021
Abstract The effective entity recognition method can quickly and accurately identify the citation entity to facilitate citation comparison, thereby reducing the occurrence of academic fraud and other behaviors. But there is no very effective way to solve this problem till now. In recent years, neural network models for named entity recognition (NER) have shown better performances on general domain datasets. After the multi‐feature citation dataset is created, the article proposes contextual multi‐feature embedding (CMFE) method for word embedding which use multi‐feature to enhance semantic and use CNN to get multi‐level feature. Based on CMFE, a multi‐feature semantic fusion model (MFSFM) is proposed. It designs the multi‐convolution kernel mixed residual CNN module to obtain local attention information and enhance the sensitivity of the entity boundary information. The BiLSTM and LSTM is used for timing learning. The experimental results of Chinese citation datasets and Chinese–English mixed citation datasets show that CMFE can better represent semantics, and MFSFM can perform citation entity recognition well. Finally, the experimental results of CONLL2003 dataset show that it is general on NER.