# Classification of Natural Language Processing Techniques for Requirements Engineering

arXiv: https://arxiv.org/abs/2204.04282 · HTML: https://mlsanity.com/paper/2204.04282/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 153 references that are in the map.
- First posted on arXiv 8 Apr 2022.

## Builds on (most cited first, 19 of 19)

- [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)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach](https://mlsanity.com/paper/1909.00161/) (2019)
- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Text Classification Algorithms: A Survey](https://mlsanity.com/paper/1904.08067/) (2019)
- [Deep vs. shallow networks : An approximation theory perspective](https://mlsanity.com/paper/1608.03287/) (2016)
- [Comparing BERT against traditional machine learning text classification](https://mlsanity.com/paper/2005.13012/) (2020)
- [Semantically Enhanced Software Traceability Using Deep Learning Techniques](https://mlsanity.com/paper/1804.02438/) (2018)
- [Rapid quality assurance with Requirements Smells](https://mlsanity.com/paper/1611.08847/) (2016)
- [Classifying Multilingual User Feedback using Traditional Machine Learning and Deep Learning](https://mlsanity.com/paper/1909.05504/) (2019)
- [Modelling Radiological Language with Bidirectional Long Short-Term Memory Networks](https://mlsanity.com/paper/1609.08409/) (2016)
- [Towards Understanding and Detecting Fake Reviews in App Stores](https://mlsanity.com/paper/1904.12607/) (2019)
- [What Works Better? A Study of Classifying Requirements](https://mlsanity.com/paper/1707.02358/) (2017)
- [Leveraging Historical Associations between Requirements and Source Code to Identify Impacted Classes](https://mlsanity.com/paper/1808.06359/) (2018)
- [Eye of the Mind: Image Processing for Social Coding](https://mlsanity.com/paper/2001.06426/) (2020)

## Built on by (most cited first, 1 of 1)

- [ChatGPT: Jack of all trades, master of none](https://mlsanity.com/paper/2302.10724/) (2023)

## Abstract (arXiv)

Research in applying natural language processing (NLP) techniques to requirements engineering (RE) tasks spans more than 40 years, from initial efforts carried out in the 1980s to more recent attempts with machine learning (ML) and deep learning (DL) techniques. However, in spite of the progress, our recent survey shows that there is still a lack of systematic understanding and organization of commonly used NLP techniques in RE. We believe one hurdle facing the industry is lack of shared knowledge of NLP techniques and their usage in RE tasks. In this paper, we present our effort to synthesize and organize 57 most frequently used NLP techniques in RE. We classify these NLP techniques in two ways: first, by their NLP tasks in typical pipelines and second, by their linguist analysis levels. We believe these two ways of classification are complementary, contributing to a better understanding of the NLP techniques in RE and such understanding is crucial to the development of better NLP tools for RE.