This study investigated how effectively cause-effect information can be extracted from newspaper text using a simple computational method (i.e. without knowledge-based inferencing and without full parsing of sentences). An automatic method was developed for identifying and extracting cause-effect information in Wall Street Journal text using linguistic clues and pattern-matching. The set of linguistic patterns used for identifying causal relations was based on a thorough review of the literature and on an analysis of sample sentences from Wall Street Journal. The cause-effect information extracted using the method was compared with that identified by two human judges. The program successfully extracted about 68% of the causal relations identified by both judges (the intersection of the two sets of causal relations identified by the judges). Of the instances that the computer program identified as causal relations, about 25% were identified by both judges, and 64% were identified by at least one of the judges. Problems encountered are discussed.
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