A Language Model and Clue based Machine Learning Method for Discovering Technology Trends from Patent Text
Yingshi Tian, Youngho Kim, Yoonjae Jeong, Jihee Ryu, Sung-Hyon Myaeng · Jeongbo gwahaghoe nonmunji. so'peuteuweeo mich eung'yong · 2009
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.