Incremental Construction of Causal Network from News Articles

Abstract

We propose a novel method for the incremental construction of causal networks to clarify the relationships among news events. We propose the Topic-Event Causal (TEC) model as a causal network model and an incremental constructing method based on it. In the TEC model, a causal relation is expressed using a directed graph and a vertex representing an event. A vertex contains structured keywords consisting of topic keywords and an SVO tuple. An SVO tuple, which consists of a tuple of subject,verb and object keywords represent the details of the event. To obtain a chain of causal relations, vertices representing a similar event need to be detected. We reduce the time taken to detect them by restricting the calculation to topics using topic keywords. We detect them on a concept level. We propose an identification method that identifies the sense of the keywords and introduce three semantic distance methods to compare keywords. Our method detects vertices representing similar events more precisely than conventional methods. We carried out experiments to validate the proposed methods.

Causal Analysis for Supporting Users' Understanding of Investment Trusts

“…Based on this hypothesis, the method can extract a cause-and-result pair that is not explicitly written in a sentence. Ishii et al [9] proposed a "topic-event cause model" and an incremental method for constructing a causal network. In the topic-event cause model, incidents are represented as SVO tuples.…”