Automatic Extraction of Cause-Effect Information from Newspaper Text Without Knowledge-based Inferencing
Christopher S. G. Khoo, Jaklin Kornfilt, Robert N Oddy, Sung-Hyun Myaeng · Literary and Linguistic Computing · 1998
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 relationships was based on a through review of the literature and on an analysis of sample sentences from the Wall Street Journal. The cause-effect information extracted using the method was compared with that identified by two human judges. The program successfully extracted ˜68% of the causal relationships identified by both judges (the intersection of the two sets of causal relationships identified by the judges.) Of the instances that the computer program identified as causal relationships, ˜25% were identified by both judges, and 64% were identified by at least one of the judges. Problems encountered are discussed.