Text Mining Business Policy Documents: Applied Data Science in Finance

Marco Spruit, Drilon Ferati

Source Title: International Journal of Business Intelligence Research (IJBIR)11(2)

ISSN: 1947-3591|EISSN: 1947-3605|EISBN13: 9781799807162|DOI: 10.4018/IJBIR.20200701.oa1

MLA

Spruit, Marco, and Drilon Ferati. "Text Mining Business Policy Documents: Applied Data Science in Finance." IJBIR vol.11, no.2 2020: pp.28-46. http://doi.org/10.4018/IJBIR.20200701.oa1

APA

Spruit, M. & Ferati, D. (2020). Text Mining Business Policy Documents: Applied Data Science in Finance. International Journal of Business Intelligence Research (IJBIR), 11(2), 28-46. http://doi.org/10.4018/IJBIR.20200701.oa1

Chicago

Spruit, Marco, and Drilon Ferati. "Text Mining Business Policy Documents: Applied Data Science in Finance," International Journal of Business Intelligence Research (IJBIR) 11, no.2: 28-46. http://doi.org/10.4018/IJBIR.20200701.oa1

Export Reference

Favorite Full-Issue Download

View Full Text HTML

View Full Text PDF

Abstract

In a time when the employment of natural language processing techniques in domains such as biomedicine, national security, finance, and law is flourishing, this study takes a deep look at its application in policy documents. Besides providing an overview of the current state of the literature that treats these concepts, the authors implement a set of natural language processing techniques on internal bank policies. The implementation of these techniques, together with the results that derive from the experiments and expert evaluation, introduce a meta-algorithmic modelling framework for processing internal business policies. This framework relies on three natural language processing techniques, namely information extraction, automatic summarization, and automatic keyword extraction. For the reference extraction and keyword extraction tasks, the authors calculated precision, recall, and F-scores. For the former, the researchers obtained 0.99, 0.84, and 0.89; for the latter, this research obtained 0.79, 0.87, and 0.83, respectively. Finally, the summary extraction approach was positively evaluated using a qualitative assessment.