@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://preview.aclanthology.org/icon-24-ingestion/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."
}