Experiments in Candidate Phrase Selection for Financial Named Entity Extraction - A Demo

Aman Kumar, Hassan Alam, Tina Werner, Manan Vyas

Abstract
In this study we develop a system that tags and extracts financial concepts called financial named entities (FNE) along with corresponding numeric values – monetary and temporal. We employ machine learning and natural language processing methods to identify financial concepts and dates, and link them to numerical entities.
Anthology ID:
C16-2010
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations
Month:
December
Year:
2016
Address:
Osaka, Japan
Editor:
Hideo Watanabe
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
45–48
Language:
URL:
https://aclanthology.org/C16-2010
DOI:
Bibkey:
Cite (ACL):
Aman Kumar, Hassan Alam, Tina Werner, and Manan Vyas. 2016. Experiments in Candidate Phrase Selection for Financial Named Entity Extraction - A Demo. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, pages 45–48, Osaka, Japan. The COLING 2016 Organizing Committee.
Cite (Informal):
Experiments in Candidate Phrase Selection for Financial Named Entity Extraction - A Demo (Kumar et al., COLING 2016)
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