Automatic detection of causal relations for Question Answering
MultiSumQA '03 Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering - Volume 12
Finding and linking incidents in news
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Extracting Causal Knowledge Using Clue Phrases and Syntactic Patterns
PAKM '08 Proceedings of the 7th International Conference on Practical Aspects of Knowledge Management
Node-first causal network extraction for trend analysis based on web mining
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
Incremental causal network construction over event streams
Information Sciences: an International Journal
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We propose a novel method of constructing causal networks to clarify the relationships among events. Since events and their relationships continually change, our method works in an incremental manner. There are two problems in the conventional methods of constructing causal networks that use keywords representing events: 1) similar events detection to network construction is a time-consuming task, and 2) because the merge operation depends on appearing order of events there is a consistency issue in the incremental construction. In this paper, as the representation model, we propose a Topic-Event Causal network model (TEC model) in which the topic and details of an event are separately represented by using structured keywords. We cluster events by using the topic keyword and then detect similar events per each cluster. This fashion will reduce the comparison times of events. When we compute the similarity of two events in a topic, since we compare the pair of SVO tuples of vertices based on WordNet, we solve the difference in the word used and the lexical ambiguity and keep the consistency with a causal network. We also show experimental results to demonstrate its usefulness.