An enhanced computational feature selection method for medical synonym identification via bilingualism and multi-corpus training
This addresses the need for improved medical NLP in Chinese, though it is incremental as it focuses on feature selection within an existing domain.
The paper tackled the problem of low precision and recall in Chinese medical synonym identification by proposing a method that selects and combines 13 Chinese and English features, achieving 97.37% precision, 96.00% recall, and 97.33% F1 score.
Medical synonym identification has been an important part of medical natural language processing (NLP). However, in the field of Chinese medical synonym identification, there are problems like low precision and low recall rate. To solve the problem, in this paper, we propose a method for identifying Chinese medical synonyms. We first selected 13 features including Chinese and English features. Then we studied the synonym identification results of each feature alone and different combinations of the features. Through the comparison among identification results, we present an optimal combination of features for Chinese medical synonym identification. Experiments show that our selected features have achieved 97.37% precision rate, 96.00% recall rate and 97.33% F1 score.