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A Language Model and Clue based Machine Learning Method for Discovering Technology Trends from Patent Text  

Tian, Yingshi (한국과학기술원 정보통신공학과)
Kim, Young-Ho (한국과학기술원 정보통신공학과)
Jeong, Yoon-Jae (한국과학기술원 전산학과)
Ryu, Ji-Hee (한국과학기술원 전산학과)
Myaeng, Sung-Hyon (한국과학기술원 정보통신공학과)
Publication Information
Journal of KIISE:Software and Applications / v.36, no.5, 2009 , pp. 420-429 More about this Journal
Abstract
Patent text is a rich source for discovering technological trends. In order to automate such a discovery process, we attempt to identify phrases corresponding to the problem and its solution method which together form a technology. Problem and solution phrases are identified by a SVM classifier using features based on a combination of a language modeling approach and linguistic clues. Based on the occurrence statistics of the phrases, we identify the time span of each problem and solution and finally generate a trend. Based on our experiment, we show that the proposed semantic phrase identification method is promising with its accuracy being 77% in R-precision. We also show that the unsupervised method for discovering technological trends is meaningful.
Keywords
Patent; textual-data mining; technological trend discovery; semantic keyphrase extraction;
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