Semantic Frames to Predict Stock Price Movement

This work introduces a novel tree representation, and uses it to train predictive models with tree kernels using support vector machines, and shows that features derived from semantic frame parsing have significantly better performance across years on the polarity task.

Abstract

Semantic frames are a rich linguistic re-source. 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. Seman-tic frames help to generalize from spe-cific sentences to scenarios, and to de-tect the (positive or negative) roles of spe-cific companies. We introduce a novel tree representation, and use it to train predic-tive models with tree kernels using sup-port vector machines. Our experiments test multiple text representations on two binary classification tasks, change of price and polarity. Experiments show that fea-tures derived from semantic frame pars-ing have significantly better performance across years on the polarity task. 1