mlsanityData to 7 Oct 2026

PET: An Annotated Dataset for Process Extraction from Natural Language Text

arXiv 2203.04860 ↗First posted 9 Mar 2022cs.CLcited by 15

Not on the map. Why?

Abstract (arXiv)

Process extraction from text is an important task of process discovery, for which various approaches have been developed in recent years. However, in contrast to other information extraction tasks, there is a lack of gold-standard corpora of business process descriptions that are carefully annotated with all the entities and relationships of interest. Due to this, it is currently hard to compare the results obtained by extraction approaches in an objective manner, whereas the lack of annotated texts also prevents the application of data-driven information extraction methodologies, typical of the natural language processing field. Therefore, to bridge this gap, we present the PET dataset, a first corpus of business process descriptions annotated with activities, gateways, actors, and flow information. We present our new resource, including a variety of baselines to benchmark the difficulty and challenges of business process extraction from text. PET can be accessed via huggingface.co/datasets/patriziobellan/PET

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 0 of its 7 references that are in the map. First posted on arXiv 9 Mar 2022.

Builds on

0 of 7 references.

No references here yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 5; 2025: 3; 2026: 3.

15 papers.

Sort:

By area:

  1. 2023Large Language Models can accomplish Business Process Management Taskscited by 34
  2. 2024ProMoAI: Process Modeling with Generative AIcited by 12
  3. 2023Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Textcited by 8
  4. 2024Towards a Benchmark for Causal Business Process Reasoning with LLMscited by 7
  5. 2023Conversational Process Modeling: Can Generative AI Empower Domain Experts in Creating and Redesigning Process Models?cited by 4
  6. 2024PAGED: A Benchmark for Procedural Graphs Extraction from Documentscited by 4
  7. 2024NLP4PBM: A Systematic Review on Process Extraction using Natural Language Processing with Rule-based, Machine and Deep Learning Methodscited by 3
  8. 2024Recent Advances in Data-Driven Business Process Managementcited by 2
  9. 2023Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Textscited by 1
  10. 2025Evaluating the Process Modeling Abilities of Large Language Models -- Preliminary Foundations and Resultscited by 1
  11. 2025Leveraging Machine Learning and Enhanced Parallelism Detection for BPMN Model Generation from Textcited by 1
  12. 2025What is the Best Process Model Representation? A Comparative Analysis for Process Modeling with Large Language Modelscited by 1
3 more
  1. 2026COVENANT: Natural-Language Workflow Compilation for Aligned Agent Executioncited by 1
  2. 2026Multi-Agent Procedural Graph Extraction with Structural and Logical Refinementcited by 0
  3. 2026Pragmos: A Process Agentic Modeling Systemcited by 0