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STAMP 4 NLP – an agile framework for rapid quality-driven NLP applications development

  • The progress in natural language processing (NLP) research over the last years, offers novel business opportunities for companies, as automated user interaction or improved data analysis. Building sophisticated NLP applications requires dealing with modern machine learning (ML) technologies, which impedes enterprises from establishing successful NLP projects. Our experience in applied NLP research projects shows that the continuous integration of research prototypes in production-like environments with quality assurance builds trust in the software and shows convenience and usefulness regarding the business goal. We introduce STAMP 4 NLP as an iterative and incremental process model for developing NLP applications. With STAMP 4 NLP, we merge software engineering principles with best practices from data science. Instantiating our process model allows efficiently creating prototypes by utilizing templates, conventions, and implementations, enabling developers and data scientists to focus on the business goals. Due to our iterative-incremental approach, businesses can deploy an enhanced version of the prototype to their software environment after every iteration, maximizing potential business value and trust early and avoiding the cost of successful yet never deployed experiments.

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Metadaten
Author:Philipp Kohl, Oliver Schmidts, Lars KlöserORCiD, Henri Werth, Bodo Kraft, Albert Zündorf
DOI:https://doi.org/10.1007/978-3-030-85347-1_12
ISBN:978-3-030-85346-4
ISBN:978-3-030-85347-1
Parent Title (English):Quality of Information and Communications Technology. QUATIC 2021
Publisher:Springer
Place of publication:Cham
Document Type:Conference Proceeding
Language:English
Year of Completion:2021
Date of first Publication:2021/08/25
Date of the Publication (Server):2021/09/16
Tag:Machine learning; Natural language processing; Process model
First Page:156
Last Page:166
Note:
International Conference on the Quality of Information and Communications Technology, QUATIC 2021, 8-11 September, Algarve, Portugal
Link:https://doi.org/10.1007/978-3-030-85347-1_12
Zugriffsart:bezahl
Institutes:FH Aachen / Fachbereich Medizintechnik und Technomathematik