Abstract
With the development of industry and technology innovation in China, the number of patent applications in the field of machinery has been increasing year by year. Mechanical patents contain rich and latest technical and legal information. The mining of mechanical patents play an important practical role. In knowledge graph construction and infringement judgment for mechanical patents, extracting the technology and efficacy is an important step. In this paper, we propose a model combining bidirectional long and short-term memory neural network and conditional random field to achieve the technology and efficacy extraction from mechanical patents abstract texts. The model in this paper achieves an accuracy rate of 94.31%, a recall rate of 94.34%, and an F1 value of 94.31%. The results show that the model in this paper can accurately extract the technology and efficacy of patents in the mechanical field.
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Cui, R., Deng, N., Zheng, C. (2023). Technology and Efficacy Extraction of Mechanical Patents Based on BiLSTM-CRF. In: Barolli, L. (eds) Advances in Internet, Data & Web Technologies. EIDWT 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 161. Springer, Cham. https://doi.org/10.1007/978-3-031-26281-4_22
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DOI: https://doi.org/10.1007/978-3-031-26281-4_22
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