关键词: 文本挖掘, Stanford Parser, 文本预处理, 依存关系, 信息抽取

Abstract: As the quantity of literature increases dramatically, to get the information manully can’t adapt to the speed of added literature. This paper proposes a new model of biological data mining, utilizing some tools of open source such as Stanford Parser, using some approaches such as natural language processing and statistics. It also analyzes its crucial technique. During the process to test the SBQTL（Soybean Quantitative Trait Loci） using this model, the precision and recall rate are 93.0% and 78.4% respectively. During the process to test the PubMed, the precision and recall rate are 94.3% and 80.0% respectively. So the problem that the researchers who are engaged in biomedicine can find the information they need from large quantity of literature quickly and efficiently is solved, and biologists can find closet information in biomedicine and verificate the newest science discovery. Thus, people can better understand the phenomenon of biomedicine.

Key words: text mining, Stanford Parser, text preprocessing, dependencies, information extraction