[Objective] This paper tries to improve the accuracy of patent keyword extraction with the characteristics of patent claims. [Methods] We examined the restriction relationship between technical features of patent claims. Then, we integrated these relationship into the patent keyword extraction method based on graph. [Results] We examined our model with the USPTO and Baiten data sets for patents. The MRR index of our method was 31.79% (USPTO) and 33.81% (Baiten) higher than the traditional TextRank method. [Limitations] The data of our experimental analysis need to be further expanded. [Conclusions] The proposed method could significantly improve the accuracy of patent keyword extraction.
Mihalcea R, Tarau P. TextRank: Bringing Order into Texts[C]// Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing. 2004: 404-411.
[2]
Wan X J, Xiao J G. Single Document Keyphrase Extraction Using Neighborhood Knowledge[C]// Proceedings of the 23rd National Conference on Artificial Intelligence. 2008: 855-860.
(Xia Tian. Study on Keyword Extraction Using Word Position Weighted TextRank[J]. New Technology of Library and Information Service, 2013(9): 30-34.)
[4]
Florescu C, Caragea C. PositionRank: An Unsupervised Approach to Keyphrase Extraction from Scholarly Documents[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 2017: 1105-1115.
(Li Hang, Tang Chaolan, Yang Xian, et al. TextRank Keyword Extraction Based on Multi Feature Fusion[J]. Journal of Intelligence, 2017, 36(8): 183-187.)
(Liu Zhuchen, Chen Hao, Yu Yanhua, et al. Extracting Keywords with TextRank and Weighted Word Positions[J]. Data Analysis and Knowledge Discovery, 2018, 2(9): 74-79.)
[7]
Boudin F. Unsupervised Keyphrase Extraction with Multipartite Graphs[C]// Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. 2018: 667-672.
(Gu Yijun, Xia Tian. Study on Keyword Extraction with LDA and TextRank Combination[J]. New Technology of Library and Information Service, 2014(7): 41-47.)
(Liu Xiaojian, Xie Fei, Wu Xindong. Graph Based Keyphrase Extraction Using LDA Topic Model[J]. Journal of the China Society for Scientific and Technical Information, 2016, 35(6): 664-672.)
(Ning Jianfei, Liu Jiangzhen. Using Word2vec with TextRank to Extract Keywords[J]. New Technology of Library and Information Service, 2016(6): 20-27.)
[12]
Wang R, Liu W, McDonald C. Using Word Embeddings to Enhance Keyword Identification for Scientific Publications [A]// Databases Theory and Applications[M]. Springer, Cham. 2015.
(Yu Yan, Shang Mingjie, Zhao Naixuan. Patent Keyword Extraction Driven by Claim Features[J]. Journal of the China Society for Scientific and Technical Information, 2021, 40(6): 610-620.)
[14]
Witten I H, Paynter G W, Frank E, et al. KEA: Practical Automatic Keyphrase Extraction[C]// Proceedings of the 4th ACM Conference on Digital Libraries. 1999: 254-255.
[15]
Zhang K, Xu H, Tang J, et al. Keyword Extraction Using Support Vector Machine[C]// Proceedings of the 7th International Conference on Advances in Web-Age Information Management. 2006: 85-96.
(Chen Yiqun, Zhou Ruqi, Zhu Weiheng, et al. Mining Patent Knowledge for Automatic Keyword Extraction[J]. Journal of Computer Research and Development, 2016, 53(8): 1740-1752.)
[17]
Hu J, Li S B, Yao Y, et al. Patent Keyword Extraction Algorithm Based on Distributed Representation for Patent Classification[J]. Entropy (Basel, Switzerland), 2018, 20(2): Ariticle No.104.
[18]
Zhang C, Wang H, Liu Y, et al. Automatic Keyword Extraction from Documents Using Conditional Random Fields[J]. Journal of Computer Information Systems, 2008, 4(3): 1169-1180.
[19]
Gollapalli S D, Li X L, Yang P. Incorporating Expert Knowledge into Keyphrase Extraction[C]// Proceedings of the 31st AAAI Conference on Artificial Intelligence. 2017: 3180-3187.
(Cheng Bin, Shi Shuicai, Du Yuncheng, et al. Keyword Extraction for Journals Based on Part-of-Speech and BiLSTM-CRF Combined Model[J]. Data Analysis and Knowledge Discovery, 2021, 5(3): 101-108.)
(Chen Wei, Wu Youzheng, Chen Wenliang, et al. Automatic Keyword Extraction Based on BiLSTM-CRF[J]. Computer Science, 2018, 45(S): 91-113.)
[22]
Sterckx L, Demeester T, Deleu J, et al. Creation and Evaluation of Large Keyphrase Extraction Collections with Multiple Opinions[J]. Language Resources and Evaluation, 2018, 52(2): 503-532.
doi: 10.1007/s10579-017-9395-6
[23]
Wang L, Li F. SJTULTLAB: Chunk Based Method for Keyphrase Extraction[C]// Proceedings of the 5th International Workshop on Semantic Evaluation. 2010: 158-161.
(Niu Ping, Huang Degen. TF-IDF and Rules Based Automatic Extraction of Chinese Keywords[J]. Journal of Chinese Computer Systems, 2016, 37(4): 711-715.)
[28]
Joung J, Kim K. Monitoring Emerging Technologies for Technology Planning Using Technical Keyword Based Analysis from Patent Data[J]. Technological Forecasting and Social Change, 2017, 114: 281-292.
doi: 10.1016/j.techfore.2016.08.020
[29]
Brin S, Page L. The Anatomy of a Large-Scale Hypertextual Web Search Engine[J]. Computer Networks and ISDN Systems, 1998, 30(1-7): 107-117.
doi: 10.1016/S0169-7552(98)00110-X