Knowledge-based acquisition of causal relationships in text
Knowledge Acquisition
COATIS, an NLP System to Locate Expressions of Actions Connected by Causality Links
EKAW '97 Proceedings of the 10th European Workshop on Knowledge Acquisition, Modeling and Management
Information Extraction: Techniques and Challenges
SCIE '97 International Summer School on Information Extraction: A Multidisciplinary Approach to an Emerging Information Technology
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Extracting causal knowledge from a medical database using graphical patterns
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
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SEKE2 is a semantic expectation-based knowledge extraction system for extracting causation relations from natural language texts. It is inspired by capitalizing the human behavior of analyzing information with semantic expectations. The framework of SEKE2 consists of different kinds of generic templates organized in a hierarchical fashion. All kinds of templates are domain independent. They are robust and enable flexible changes for different domains and expected semantics. By associating a causation semantic template with a set of sentence templates, SEKE2 can extract causation knowledge from complex sentences without full-fledged syntactic parsing. To demonstrate the flexibility of SEKE2 for different domains, we study the application of causation semantic templates on two domain areas of news stories, namely, Hong Kong stock market movement and global warming.