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.
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Acknowledgments
This work was supported by National Key Research and Development Program of China under Grant 2017YFC1405403; National Natural Science Foundation of China under Grant 61075059; Philosophical and Social Sciences Research Project of Hubei Education Department under Grant 19Q054; Green Industry Technology Leading Project (product development category) of Hubei University of Technology under Grant CPYF2017008; Research Foundation for Advanced Talents of Hubei University of Technology under Grant BSQD12131; Natural Science Foundation of Anhui Province under Grant 1708085MF161; and Key Project of Natural Science Research of Universities in Anhui under Grant KJ2015A236.
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Deng, N., Chen, X., Xiong, C. (2020). Semantic Similarity Calculation of TCM Patents in Intelligent Retrieval Based on Deep Learning. In: Barolli, L., Hellinckx, P., Natwichai, J. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2019. Lecture Notes in Networks and Systems, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-030-33509-0_44
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DOI: https://doi.org/10.1007/978-3-030-33509-0_44
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