Abstract
Patent prior art search uses dispersed information to retrieve all the relevant documents with strong ambiguity from the massive patent database. This challenging task consists in patent reduction and patent expansion. Existing studies on patent reduction ignore the relevance between technical characteristics and technical domains, and result in ambiguous queries. Works on patent expansion expand terms from external resource by selecting words with similar distribution or similar semantics. However, this splits the relevance between the distribution and semantics of the terms. Besides, common repository hardly meets the requirement of patent expansion for uncommon semantics and unusual terms. In order to solve these problems, we first present a novel composite-domain perspective model which converts the technical characteristic of a query patent to a specific composite classified domain and generates aspect queries. We then implement patent expansion with double consistency by combining distribution and semantics simultaneously.We also propose to train semantic vector spaces via word embedding under the specific classified domains, so as to provide domain-aware expanded resource. Finally, multiple retrieval results of the same topic are merged based on perspective weight and rank in the results. Our experimental results on CLEP-IP 2010 demonstrate that our method is very effective. It reaches about 5.43% improvement in recall and nearly 12.38% improvement in PRES over the state-of-the-art. Our work also achieves the best performance balance in terms of recall, MAP and PRES.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61232002, 61572376), the Science and Technology Support Program of Hubei Province (2015BAA127) and the Wuhan Innovation Team Project (2014070504020237).
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Fei Wang is a PhD candidate at the School of Computer Science, Wuhan University, China. His current research interests are in the database, complex data management, patent mining, information retrieval and natural language processing. He received the ME degree in Computer Science from Chengdu University of Information Technology, China in 2014.
Tieyun Qian is a professor at the State Key Laboratory of Software Engineering at Wuhan University, China. She received her BS degree in computer science from Wuhan University of Technology, China in 1991, and her PhD degree in computer science from Huazhong University of Science and Technology, China in 2006. Her current research interests include text mining, Web mining, and natural language processing. She has published over 30 papers in leading conferences including ACL, EMNLP, SIGIR, etc. She is a member of CCF and ACM. She has served as program committee member of many premium conferences: WWW, COLING, DASFAA, WAIM, and APWeb.
Bin Liu is a lecture at the School of Computer Science, Wuhan University, China. Bin Liu received the PhD, BS, and ME degree in Computer Science from Wuhan University, China. His current research interests are in the database, data mining, complex data management and natural language processing.
Zhiyong Peng received the BS and ME degree in Computer Science from Wuhan University and Changsha Institute of Technology of China, respectively. He received PhD degree from Kyoto University of Japan in 1995. He is a professor at Wuhan University. Prior to join Wuhan University in 2000, he worked as a researcher at the Advanced Software Technology and Mechatronics Research Institute of Kyoto from 1995 to 1997 and was a member of the technical staff at Hewlett-Packard Laboratories, Japan from 1997 to 2000. His current research interests are in the database, trusted data management, and complex data management.
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Wang, F., Qian, T., Liu, B. et al. Patent expanded retrieval via word embedding under composite-domain perspectives. Front. Comput. Sci. 13, 1048–1061 (2019). https://doi.org/10.1007/s11704-018-7056-6
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DOI: https://doi.org/10.1007/s11704-018-7056-6