著者

Hiroshi Ishii Qiang Ma Masatoshi Yoshikawa

出版者

Information Processing Society of Japan

雑誌

Journal of Information Processing (ISSN:18826652)

巻号頁・発行日

vol.20, no.1, pp.207-215, 2012 (Released:2012-01-15)

参考文献数

20

被引用文献数

1 7

---

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.