Extracting causal knowledge by time series analysis of events

Causal knowledge is important for decision-making and risk aversion. However, it takes much time and effort to extract causal knowledge manually from a large-scale corpus. Therefore, many studies have proposed several methods for automatically extracting causal knowledge. These methods use a variety of linguistic or textual cues indicating causality on the basis of the assumption that causally related events tend to co-occur within a document. However, because of this assumption, they cannot extract causal knowledge that is not explicitly described in a document. Therefore, in this paper, we propose a novel method for extracting causal knowledge not explicitly described in a document using time series analysis of events. In our method, event expressions, which are represented by a pair of a noun phrase and a verb phrase, are extracted from newspaper articles. These extracted event expressions are clustered into distinct events, and the burst of the appearance of these clustered events is detected. Finally, using the time series data with burst, it is judged whether any event pairs have a causal relationship by Granger Causality test. We demonstrate through an evaluation experiment that the proposed method successfully extracts valid causal knowledge, almost all of which cannot be extracted by existing cue-based methods. © 2015, Japanese Society for Artificial Intelligence. All rights reserved.