Semantic Similarity Calculation of TCM Patents in Intelligent Retrieval Based on Deep Learning

Abstract

Semantic similarity calculation between words is an important step of text analysis, mining and intelligent retrieval. It can help to achieve intelligent retrieval at the semantic level and improve the accuracy and recall rate of retrieval. Because of the particularity of TCM (Traditional Chinese Medicine) patents and the insufficiency of research, most of the current mainstream TCM patent retrieval systems are keywords-based, and the retrieval results are not satisfactory. In order to improve the intelligence level of TCM patent retrieval, to promote TCM innovation and avoid repetitive research, based on real TCM patent corpus, this paper utilizes the excellent feature learning ability of deep learning to build a neural network model, and gives a method to calculate the semantic similarity between words in TCM patents. The experimental results show that the proposed method is effective. In addition, this method can be extended to semantic similarity calculation in other domains.