# Text Annotation Handbook: A Practical Guide for Machine Learning Projects

arXiv: https://arxiv.org/abs/2310.11780 · HTML: https://mlsanity.com/paper/2310.11780/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 18 references that are in the map.
- First posted on arXiv 18 Oct 2023.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Playing with Words at the National Library of Sweden -- Making a Swedish BERT](https://mlsanity.com/paper/2007.01658/) (2020)
- [Data Readiness for Natural Language Processing](https://mlsanity.com/paper/2009.02043/) (2020)

## Abstract (arXiv)

This handbook is a hands-on guide on how to approach text annotation tasks. It provides a gentle introduction to the topic, an overview of theoretical concepts as well as practical advice. The topics covered are mostly technical, but business, ethical and regulatory issues are also touched upon. The focus lies on readability and conciseness rather than completeness and scientific rigor. Experience with annotation and knowledge of machine learning are useful but not required. The document may serve as a primer or reference book for a wide range of professions such as team leaders, project managers, IT architects, software developers and machine learning engineers.