mlsanityData to 14 Oct 2026

NLP4PBM: A Systematic Review on Process Extraction using Natural Language Processing with Rule-based, Machine and Deep Learning Methods

arXiv 2409.13738 ↗First posted 10 Sep 2024cs.CLcited by 3

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Abstract (arXiv)

This literature review studies the field of automated process extraction, i.e., transforming textual descriptions into structured processes using Natural Language Processing (NLP). We found that Machine Learning (ML) / Deep Learning (DL) methods are being increasingly used for the NLP component. In some cases, they were chosen for their suitability towards process extraction, and results show that they can outperform classic rule-based methods. We also found a paucity of gold-standard, scalable annotated datasets, which currently hinders objective evaluations as well as the training or fine-tuning of ML / DL methods. Finally, we discuss preliminary work on the application of LLMs for automated process extraction, as well as promising developments in this field.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 23 of its 98 references that are in the map. First posted on arXiv 10 Sep 2024.

Builds on

23 of 98 references.

References · 23

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11 more
  1. 2021An End-to-end Model for Entity-level Relation Extraction using Multi-instance Learningcited by 31
  2. 2024Process Modeling With Large Language Modelscited by 26
  3. 2023AceCoder: Utilizing Existing Code to Enhance Code Generationcited by 25
  4. 2023Large Language Models for Business Process Management: Opportunities and Challengescited by 24
  5. 2023Abstractions, Scenarios, and Prompt Definitions for Process Mining with LLMs: A Case Studycited by 20
  6. 2022Retrieval-Augmented Generative Question Answering for Event Argument Extractioncited by 18
  7. 2023Just Tell Me: Prompt Engineering in Business Process Managementcited by 17
  8. 2019An Approach for Process Model Extraction By Multi-Grained Text Classificationcited by 16
  9. 2022PET: An Annotated Dataset for Process Extraction from Natural Language Textcited by 15
  10. 2023Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Textcited by 8
  11. 2024Leveraging Data Augmentation for Process Information Extractioncited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Knowledge-Driven Hallucination in Large Language Models: An Empirical Study on Process Modelingcited by 1
  2. 2026Ambiguity Detection and Elimination in Automated Executable Process Modelingcited by 0
  3. 2026A Hybrid LLM-Based Framework for Automated Security Annotation Generation in Business Process Modelscited by 0