Company Mention Detection for Large Scale Text Mining

Rebecca J. Passonneau, Tifara Ramelson, Boyi Xie

2014

Abstract

Text mining on a large scale that addresses actionable prediction needs to contend with noisy information in documents, and with interdependencies between the NLP techniques applied and the data representation. This paper presents an initial investigation of the impact of improved company mention detection for financial analytics using Named Entity recognition and coreference. Coverage of company mention detection improves dramatically. Improvement for prediction of stock price varies, depending on the data representation.

Paper Citation

in Harvard Style

J. Passonneau R., Ramelson T. and Xie B. (2014). Company Mention Detection for Large Scale Text Mining . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: SSTM, (IC3K 2014) ISBN 978-989-758-048-2, pages 512-520. DOI: 10.5220/0005174405120520

in Bibtex Style

@conference{sstm14,
author={Rebecca J. Passonneau and Tifara Ramelson and Boyi Xie},
title={Company Mention Detection for Large Scale Text Mining},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: SSTM, (IC3K 2014)},
year={2014},
pages={512-520},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005174405120520},
isbn={978-989-758-048-2},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: SSTM, (IC3K 2014)
TI - Company Mention Detection for Large Scale Text Mining
SN - 978-989-758-048-2
AU - J. Passonneau R.
AU - Ramelson T.
AU - Xie B.
PY - 2014
SP - 512
EP - 520
DO - 10.5220/0005174405120520