Paper: Semantic Frames to Predict Stock Price Movement

ACL ID P13-1086
Title Semantic Frames to Predict Stock Price Movement
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013

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.