This document concerns data readiness in the context of machine learning and Natural Language Processing. It describes how an organization may proceed to identify, make available, validate, and prepare data to facilitate automated analysis methods.
The contents of the document is based on the practical challenges and frequently asked questions we (NLU group at RISE) have encountered in our work as an applied research institute with helping organizations and companies, both in the public and private sectors, to use data in their business processes.
The purpose of this document is to describe how an organization may proceed to identify, make available, validate, and prepare data to facilitate automated analysis methods (machine learning, natural language processing).
- Click here for the latest working version at Read the Docs.
- Click here for the latest working version at GitHub.
- Data Readiness for Natural Language Processing, Olsson & Sahlgren, 2020.
- We Need to Talk About Data: The Importance of Data Readiness in Natural Language Processing, Olsson & Sahlgren, 2021.
The NLP data readiness document is work-in-progress. If you have any questions, suggestions for edits, or other input, please email the author (fredrik.olsson AT-SIGN gavagai.io) or submit a pull request in the document's GitHub repository, available at: https://github.com/fredriko/nlp-data-readiness.
General step-by-step instructions for how to contribute to create a pull request is descibed by, e.g, Step-by-step guide to contributing on GitHub.