Abstract
Patents are the greatest source of technical information in the world. High-efficient patent mining technologies are of great help to technical innovations and protection of intellectual property right. The extraction of technology effect clauses or phrases is an important research area in patent mining. Due to the specialty and uniqueness of patent data, traditional keyword extraction algorithms cannot properly apply to the extraction of technology effect phrases, leaving it dependent on high-cost manual processing. We propose a semi-automatic method based on partitioning corpus to extract technology effect phrases in Chinese patent abstracts. Experiments show that this method achieves satisfying precision and recall while involving little human labor.
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References
Alberts, D., et al.: Introduction to patent searching. In: Currentchallenges in Patent Information Retrieval, pp. 3–43. Springer, Heidelberg (2011)
Chen, Y., Zhang, X.: Research on the identification of technology effect phrases in patents. Contemporary Technology in Library and Information Science 12, 010 (2011)
Chen, X., Peng, Z., Zeng, C.: A co-training based method for chinese patentsemantic annotation. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management. ACM (2012)
Nanba, H., Fujii, A., Iwayama, M., Hashimoto, T.: Overview of the patent mining task at the NTCIR-8 workshop. In: Proceeding of the Eighth NTCIR Workshop, Tokyo, Japan, June 15-18, pp. 293–302 (2010)
Parapatics, P., Dittenbach, M.: Patent claim decomposition for improvedinformation extraction. In: Current Challenges in Patent Information Retrieval, pp. 197–216. Springer, Heidelberg (2011)
Matsuo, Y., Ishizuka, M.: Keyword extraction from a single document using wordco-occurrence statistical information. International Journal on Artificial Intelligence Tools 13(01), 157–169 (2004)
Ni, W., Liu, T., Zeng, Q.: An Under-Sampling Approach to ImbalancedAutomatic Keyphrase Extraction. In: Gao, H., Lim, L., Wang, W., Li, C., Chen, L. (eds.) WAIM 2012. LNCS, vol. 7418, pp. 387–398. Springer, Heidelberg (2012)
Levy, R., Manning, C.: Is it harder to parse Chinese, or the Chinese Treebank? In: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, vol. 1. Association for Computational Linguistics (2003)
The Stanford Parser: A statistical parser, http://nlp.stanford.edu/software/lex-parser.shtml
Zhang, H.-P., et al.: HHMM-based Chinese lexical analyzer ICTCLAS. In: Proceedings of the Second SIGHAN Workshop on Chinese Language Processing, vol. 17. Association for Computational Linguistics (2003)
Yuuji, K.: Inkjet Printer: China, 02154598.7[P]. 2003-06-18
Xue, X., Bruce Croft, W.: Automatic query generation for patent search. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management. ACM (2009)
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Liu, D., Peng, Z., Liu, B., Chen, X., Guo, Y. (2014). Technology Effect Phrase Extraction in Chinese Patent Abstracts. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8709. Springer, Cham. https://doi.org/10.1007/978-3-319-11116-2_13
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DOI: https://doi.org/10.1007/978-3-319-11116-2_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11115-5
Online ISBN: 978-3-319-11116-2
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