# An Approach for Process Model Extraction By Multi-Grained Text Classification

arXiv: https://arxiv.org/abs/1906.02127 · HTML: https://mlsanity.com/paper/1906.02127/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2019.

## Built on by (most cited first, 16 of 16)

- [Constructing Flow Graphs from Procedural Cybersecurity Texts](https://mlsanity.com/paper/2105.14357/) (2021)
- [Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text](https://mlsanity.com/paper/2305.03960/) (2023)
- [MAO: A Framework for Process Model Generation with Multi-Agent Orchestration](https://mlsanity.com/paper/2408.01916/) (2024)
- [A Universal Prompting Strategy for Extracting Process Model Information from Natural Language Text using Large Language Models](https://mlsanity.com/paper/2407.18540/) (2024)
- [Machine learning in business process management: A systematic literature review](https://mlsanity.com/paper/2405.16396/) (2024)
- [Process Extraction from Text: Benchmarking the State of the Art and Paving the Way for Future Challenges](https://mlsanity.com/paper/2110.03754/) (2021)
- [PAGED: A Benchmark for Procedural Graphs Extraction from Documents](https://mlsanity.com/paper/2408.03630/) (2024)
- [Leveraging pre-trained language models for conversational information seeking from text](https://mlsanity.com/paper/2204.03542/) (2022)
- [NLP4PBM: A Systematic Review on Process Extraction using Natural Language Processing with Rule-based, Machine and Deep Learning Methods](https://mlsanity.com/paper/2409.13738/) (2024)
- [Recent Advances in Data-Driven Business Process Management](https://mlsanity.com/paper/2406.01786/) (2024)
- [Knowledge-Driven Hallucination in Large Language Models: An Empirical Study on Process Modeling](https://mlsanity.com/paper/2509.15336/) (2025)
- [GUIDO: A Hybrid Approach to Guideline Discovery & Ordering from Natural Language Texts](https://mlsanity.com/paper/2307.09959/) (2023)
- [Business Process Text Sketch Automation Generation Using Large Language Model](https://mlsanity.com/paper/2309.01071/) (2023)
- [Unsupervised Learning of Graph from Recipes](https://mlsanity.com/paper/2401.12088/) (2024)
- [Assisted Data Annotation for Business Process Information Extraction from Textual Documents](https://mlsanity.com/paper/2410.01356/) (2024)
- [Business as Rulesual: A Benchmark and Framework for Business Rule Flow Modeling with LLMs](https://mlsanity.com/paper/2505.18542/) (2025)

## Abstract (arXiv)

Process model extraction (PME) is a recently emerged interdiscipline between natural language processing (NLP) and business process management (BPM), which aims to extract process models from textual descriptions. Previous process extractors heavily depend on manual features and ignore the potential relations between clues of different text granularities. In this paper, we formalize the PME task into the multi-grained text classification problem, and propose a hierarchical neural network to effectively model and extract multi-grained information without manually-defined procedural features. Under this structure, we accordingly propose the coarse-to-fine (grained) learning mechanism, training multi-grained tasks in coarse-to-fine grained order to share the high-level knowledge for the low-level tasks. To evaluate our approach, we construct two multi-grained datasets from two different domains and conduct extensive experiments from different dimensions. The experimental results demonstrate that our approach outperforms the state-of-the-art methods with statistical significance and further investigations demonstrate its effectiveness.