Extracting Complex Relations from Banking Documents

Berke Oral, Erdem Emekligil, Seçil Arslan, Gülşen Eryiğit


Abstract
In order to automate banking processes (e.g. payments, money transfers, foreign trade), we need to extract banking transactions from different types of mediums such as faxes, e-mails, and scanners. Banking orders may be considered as complex documents since they contain quite complex relations compared to traditional datasets used in relation extraction research. In this paper, we present our method to extract intersentential, nested and complex relations from banking orders, and introduce a relation extraction method based on maximal clique factorization technique. We demonstrate 11% error reduction over previous methods.
Anthology ID:
D19-5101
Volume:
Proceedings of the Second Workshop on Economics and Natural Language Processing
Month:
November
Year:
2019
Address:
Hong Kong
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–9
Language:
URL:
https://www.aclweb.org/anthology/D19-5101
DOI:
10.18653/v1/D19-5101
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-5101.pdf