Extracting Causation Knowledge from Natural Language Texts

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.