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Authors: Rebecca J. Passonneau 1 ; Tifara Ramelson 2 and Boyi Xie 1

Affiliations: 1 Columbia University, United States ; 2 Brandeis University, United States

Keyword(s): Text Mining For Financial News, Financial Analytics, Coreference Resolution, Named Entity Recognition.

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.

CC BY-NC-ND 4.0

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Paper citation in several formats:
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 (IC3K 2014) - SSTM; ISBN 978-989-758-048-2; ISSN 2184-3228, SciTePress, pages 512-520. DOI: 10.5220/0005174405120520

@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 (IC3K 2014) - SSTM},
year={2014},
pages={512-520},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005174405120520},
isbn={978-989-758-048-2},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2014) - SSTM
TI - Company Mention Detection for Large Scale Text Mining
SN - 978-989-758-048-2
IS - 2184-3228
AU - J. Passonneau, R.
AU - Ramelson, T.
AU - Xie, B.
PY - 2014
SP - 512
EP - 520
DO - 10.5220/0005174405120520
PB - SciTePress