Semantic Expectation-Based Causation Knowledge Extraction: A Study on Hong Kong Stock Movement Analysis

Human beings generally analyze information with some kinds of semantic expectations. This not only speeds up the processing time, it also helps to put the analysis in the correct context and perspective. To capitalize on this type of intelligent human behavior, this paper proposes a semantic expectation-based knowledge extraction methodology (SEKE) for extracting causation relations from text. In particular, we study the application of a causation semantic template on the Hong Kong Stock market movement (Hang Seng Index) with English financial news from Reuters, South China Morning Post and Hong Kong Standard. With one-month data input and over a two-month testing period, the system shows that it can correctly analyzes single reason sentences with about 76% precision and 74% recall rates. If partial reason extraction (two out of one reason) is included and weighted by a factor of 0.5, the performance is improved to about 83% and 81% respectively. As the proposed framework is language independent, we expect cross lingual knowledge extraction can work better with this semantic expectation-based framework.