Data Readiness for Natural Language Processing

TL;DR

This paper discusses the importance of data readiness in NLP, outlining practical steps for organizations to identify, validate, and prepare data to enable effective automated analysis methods.

Contribution

It provides a practical framework and insights based on real-world challenges for organizations to improve data readiness for NLP applications.

Findings

Identifies key challenges in data preparation for NLP

Provides practical guidelines for data validation and availability

Highlights common issues faced by organizations in data readiness

Abstract

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 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.