Professional entity recognition for computer science.

To obtain professional entity information including expert research fields in academic papers and provide theoretical references for academic paper or technology project review experts, an entity recognition model based on RoBERTa-wwm is proposed to identify professional entities in academic papers in the field of computer science. First, with the reference of the available experts' basic information table, the abstract data of these experts' academic papers are obtained through the advanced search of the China National Knowledge Infrastructure ( CNKI) and crawler technology. Next, the abstract data are manually annotated and the RoBERTa-wwm pre-training model is employed to obtain character vectors with semantic features as inputs for downstream models. Finally, the semantic character vectors are put into the BiLSTM-CRF model to identify professional entity recognition in the text. The experiments show the proposed model achieves better results in the self-labeled dataset. The F1 score of model reaches 89.94%, higher than all other comparison models in the experiment, demonstrating its excellent ability to identify professional entities. [ABSTRACT FROM AUTHOR]