Semantic frames to predict stock price movement

Boyi Xie, Rebecca Jane Passonneau, Leon Wu, Germán G. Creamer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

58 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationLong Papers
PublisherAssociation for Computational Linguistics (ACL)
Pages873-883
Number of pages11
ISBN (Print)9781937284503
StatePublished - Jan 1 2013
Event51st Annual Meeting of the Association for Computational Linguistics, ACL 2013 - Sofia, Bulgaria
Duration: Aug 4 2013Aug 9 2013

Publication series

NameACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Volume1

Other

Other51st Annual Meeting of the Association for Computational Linguistics, ACL 2013
CountryBulgaria
CitySofia
Period8/4/138/9/13

All Science Journal Classification (ASJC) codes

  • Language and Linguistics
  • Linguistics and Language

Fingerprint Dive into the research topics of 'Semantic frames to predict stock price movement'. Together they form a unique fingerprint.

Cite this