# Do It Right! A Methodology for Successful NLP System Development

arXiv: https://arxiv.org/abs/2607.05644 · HTML: https://mlsanity.com/paper/2607.05644/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 42 references that are in the map.
- First posted on arXiv 6 Jul 2026.

## Builds on (most cited first, 5 of 5)

- [HuggingFace's Transformers: State-of-the-art Natural Language Processing](https://mlsanity.com/paper/1910.03771/) (2019)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change](https://mlsanity.com/paper/1606.02821/) (2016)
- [Improving Large Language Models for Clinical Named Entity Recognition via Prompt Engineering](https://mlsanity.com/paper/2303.16416/) (2023)
- [Natural Language Processing in Biomedicine: A Unified System Architecture Overview](https://mlsanity.com/paper/1401.0569/) (2014)

## Abstract (arXiv)

Natural language processing (NLP) is a common method for supplying data to clinical research and decision making by extracting information from electronic medical records. Numerous textbooks and tutorials describe specific algorithms and applications for text processing, yet algorithmic knowledge is only one ingredient of a successful NLP project. Drawing on the available literature, this paper presents a stepwise approach that applies the Systems Development Life Cycle (SDLC) to projects that rely on data extraction through language processing.