Anaphora Resolution in Chinese for Analysis of Medical Q&A Platforms

Abstract

In medical Q&A platforms, patients share information about their diagnosis, give advice and consult with doctors, this creates a large amount of data that contains valuable knowledge on the side effects of drugs, patients’ actions and symptoms. This information is widely considered to be the most important in the field of computer-aided medical analysis. Nevertheless, messages on the Internet are difficult to analyze because of their unstructured form. Thus, the purpose of this study is to develop a program for anaphora resolution in Chinese and to implement it for analysis of user-generated content in the medical Q&A platform. The experiments are conducted on three models: BERT, NeuralCoref and BERT-Chinese+SpanBERT. BERT-Chinese+SpanBERT achieves the highest accuracy—68.5% on the OntoNotes 5.0 corpus. Testing the model that showed the highest result was carried out on messages from the medical Q&A platform haodf.com. The results of the study might contribute to improving the diagnosis of hereditary diseases.