Information Extraction from Federal Open Market Committee Statements

Oana Frunza


Abstract
We present a novel approach to unsupervised information extraction by identifying and extracting relevant concept-value pairs from textual data. The system’s building blocks are domain agnostic, making it universally applicable. In this paper, we describe each component of the system and how it extracts relevant economic information from U.S. Federal Open Market Committee (FOMC) statements. Our methodology achieves an impressive 96% accuracy for identifying relevant information for a set of seven economic indicators: household spending, inflation, unemployment, economic activity, fixed in-vestment, federal funds rate, and labor market.
Anthology ID:
2020.fnp-1.32
Volume:
Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation
Month:
December
Year:
2020
Address:
Barcelona, Spain (Online)
Venues:
COLING | FNP
SIG:
Publisher:
COLING
Note:
Pages:
195–203
Language:
URL:
https://www.aclweb.org/anthology/2020.fnp-1.32
DOI:
Bib Export formats:
BibTeX MODS XML EndNote
PDF:
http://aclanthology.lst.uni-saarland.de/2020.fnp-1.32.pdf