Constituent input on regulatory initiatives : a machine-learning approach to efficiently and effectively analyze unstructured data
Daniel P. Ferguson, M. Kathleen Harris, L. Tyler Williams
| Year of publication: |
2023
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| Authors: | Ferguson, Daniel P. ; Harris, M. Kathleen ; Williams, L. Tyler |
| Published in: |
Journal of information systems : a publication of the Accounting Information Systems Section of the American Accounting Associaton. - Sarasota, Fla. : [Verlag nicht ermittelbar], ISSN 0888-7985, ZDB-ID 1176427-2. - Vol. 37.2023, 3, p. 119-138
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| Subject: | machine-learning | text mining | natural language processing | regulation | standard-setting | unstructured data | Regulierung | Regulation | Data Mining | Data mining | Text |
| Type of publication: | Article |
|---|---|
| Type of publication (narrower categories): | Aufsatz in Zeitschrift ; Article in journal |
| Language: | English |
| Other identifiers: | 10.2308/ISYS-2021-032 [DOI] |
| Source: | ECONIS - Online Catalogue of the ZBW |
Persistent link: https://www.econbiz.de/10014431258