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Semantic Frames to Predict Stock Price Movement
| Citations: | 4 - 0 self |
BibTeX
@MISC{Xie_semanticframes,
author = {Boyi Xie and Rebecca J. Passonneau and Leon Wu and Germán G. Creamer},
title = {Semantic Frames to Predict Stock Price Movement},
year = {}
}
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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
Keyphrases
semantic frame predict stock price movement general nlp problem spe-cific company polarity task tree kernel binary classification task multiple text representation semantic frame parser predic-tive model spe-cific sentence sup-port vector machine stock price rich linguistic re-source much work novel tree representation semantic frame pars-ing financial news seman-tic frame
