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:
- 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)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/C16-2010.pdf