| Item Type |
Journal paper
|
| Abstract |
Despite a large number of available techniques around Deep Learning in Natural Language Processing (NLP), no holistic framework exists which supports researchers and practitioners to organise knowledge when designing, comparing and evaluating NLP applications. This paper addresses this lack of a holistic framework by developing a taxonomy for Deep Learning in Natural Language Processing. Based on a systematic literature review as proposed by Webster and Watson [1] and vom Brocke et al. [2] and the iterative taxonomy development process of Nickerson et al. [3] we derived five novel dimensions and 38 characteristics based on a sample of 205 papers. Our research suggests, that a Deep Learning NLP approach can be distinguished by five dimensions which were partly derived from the CRISP-DM methodology: application understanding, data preparation, modeling, learning technique and evaluation. We, therefore, hope to provide guidance and support for researchers and practitioners when using Deep Learning for NLP to design, compare and evaluate NLP applications. |
| Authors |
Landolt, Severin; Wambsganss, Thiemo & Söllner, Matthias |
| Language |
English |
| Subjects |
computer science
information management |
| HSG Classification |
contribution to scientific community |
| HSG Profile Area |
SoM - Business Innovation |
| Refereed |
Yes |
| Date |
2021 |
| Publisher |
Hawaii International Conference on System Sciences |
| Place of Publication |
Hawaii |
| Depositing User |
Thiemo Wambsganss
|
| Date Deposited |
20 Nov 2020 13:35 |
| Last Modified |
20 Jul 2022 17:43 |
| URI: |
https://www.alexandria.unisg.ch/publications/261504 |
A Taxonomy for Deep Learning in Natural Language Processing
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