Semantic Frames to Predict Stock Price Movement

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

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