Semantic frames are a rich linguistic resource. There has been much work on semantic frame parsers, but less that applies them to general NLP problems. We address a task to predict change in stock price from financial news. Semantic frames help to generalize from specific sentences to scenarios, and to detect the (positive or negative) roles of specific companies. We introduce a novel tree representation, and use it to train predictive models with tree kernels using support vector machines. Our experiments test multiple text representations on two binary classification tasks, change of price and polarity. Experiments show that features derived from semantic frame parsing have significantly better performance across years on the polarity task.
REF proposed a semantic tree structure for data representation and used tree kernel for support vector learning to predict the price change based on financial news.
Semantic Frames to Predict Stock Price Movement
ACL
Computer Science