关键词：自然语言处理；化学反应；生物学；学术文献
摘 要：This paper describes a natural language processing system that generates patterns from labeled training data and uses them to extract chemical reactions from PubMed. To train and validate our system, we create a dataset using BRENDA, the BRaunschweig ENzyme DAtabase, with 4387 labeled sentences. Our system achieves a recall of 0.82 and a precision of 0.88 via cross validation. On a selection of 600,000 PubMed abstracts, our system extracts almost 20% of existing reactions in BRENDA as well as many that are novel.