# Natural Language in Requirements Engineering for Structure Inference -- An Integrative Review

arXiv: https://arxiv.org/abs/2202.05065 · HTML: https://mlsanity.com/paper/2202.05065/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 56 references that are in the map.
- First posted on arXiv 10 Feb 2022.

## Builds on (most cited first, 3 of 3)

- [Recent Trends in Deep Learning Based Natural Language Processing](https://mlsanity.com/paper/1708.02709/) (2017)
- [Automatic Detection of Causality in Requirement Artifacts: the CiRA Approach](https://mlsanity.com/paper/2101.10766/) (2021)
- [CATE: CAusality Tree Extractor from Natural Language Requirements](https://mlsanity.com/paper/2107.10023/) (2021)

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

- [Information Extraction in Domain and Generic Documents: Findings from Heuristic-based and Data-driven Approaches](https://mlsanity.com/paper/2307.00130/) (2023)

## Abstract (arXiv)

The automatic extraction of structure from text can be difficult for machines. Yet, the elicitation of this information can provide many benefits and opportunities for various applications. Benefits have also been identified for the area of Requirements Engineering. To evaluate what work has been done and is currently available, the paper at hand provides an integrative review regarding Natural Language Processing (NLP) tools for Requirements Engineering. This assessment was conducted to provide a foundation for future work as well as deduce insights from the stats quo. To conduct the review, the history of Requirements Engineering and NLP are described as well as an evaluation of over 136 NLP tools. To assess these tools, a set of criteria was defined. The results are that currently no open source approach exists that allows for the direct/primary extraction of information structure and even closed source solutions show limitations such as supervision or input limitations, which eliminates the possibility for fully automatic and universal application. As a results, the authors deduce that the current approaches are not applicable and a different methodology is necessary. An approach that allows for individual management of the algorithm, knowledge base, and text corpus is a possibility being pursued.