STAMP 4 NLP -- An Agile Framework for Rapid Quality-Driven NLP Applications Development

• URL: http://arxiv.org/abs/2111.08408v1
• Date: Tue, 16 Nov 2021 12:20:47 GMT
• Title: STAMP 4 NLP -- An Agile Framework for Rapid Quality-Driven NLP Applications Development
• Authors: Philipp Kohl and Oliver Schmidts and Lars Kl\"oser and Henri Werth and Bodo Kraft and Albert Z\"undorf
• Abstract summary: 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. Due to our iterative-incremental approach, businesses can deploy an enhanced version of the prototype to their software environment after every iteration.
• Score: 3.86574270083089
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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