@inproceedings{oral-etal-2019-extracting,
title = "Extracting Complex Relations from Banking Documents",
author = {Oral, Berke and
Emekligil, Erdem and
Arslan, Se{\c{c}}il and
Eryi{\u{g}}it, G{\"u}l{\c{s}}en},
editor = "Hahn, Udo and
Hoste, V{\'e}ronique and
Zhang, Zhu",
booktitle = "Proceedings of the Second Workshop on Economics and Natural Language Processing",
month = nov,
year = "2019",
address = "Hong Kong",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-5101/",
doi = "10.18653/v1/D19-5101",
pages = "1--9",
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."
}
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<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.</abstract>
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%0 Conference Proceedings
%T Extracting Complex Relations from Banking Documents
%A Oral, Berke
%A Emekligil, Erdem
%A Arslan, Seçil
%A Eryiğit, Gülşen
%Y Hahn, Udo
%Y Hoste, Véronique
%Y Zhang, Zhu
%S Proceedings of the Second Workshop on Economics and Natural Language Processing
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong
%F oral-etal-2019-extracting
%X 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.
%R 10.18653/v1/D19-5101
%U https://aclanthology.org/D19-5101/
%U https://doi.org/10.18653/v1/D19-5101
%P 1-9
Markdown (Informal)
[Extracting Complex Relations from Banking Documents](https://aclanthology.org/D19-5101/) (Oral et al., 2019)
ACL
- Berke Oral, Erdem Emekligil, Seçil Arslan, and Gülşen Eryiğit. 2019. Extracting Complex Relations from Banking Documents. In Proceedings of the Second Workshop on Economics and Natural Language Processing, pages 1–9, Hong Kong. Association for Computational Linguistics.