用AI从文本中自动提取流程，效果优于传统规则方法。
NLP4PBM: A Systematic Review on Process Extraction using Natural Language Processing with Rule-based, Machine and Deep Learning Methods
- 结合机器学习与深度学习，提升文本到流程的转换能力
- 实验证明新方法在流程提取任务上超越传统规则系统
- 适合关注自动化流程建模的研究者和工业应用开发者
本综述研究自动化流程提取领域，即利用自然语言处理（NLP）将文本描述转化为结构化流程。我们发现，机器学习（ML）/深度学习（DL）方法正越来越多地应用于NLP组件中。在某些情况下，这些方法因其对流程提取任务的适配性而被选用，实验结果表明它们可优于经典规则方法。同时，我们发现高质量、可扩展的标注数据集严重匮乏，这目前阻碍了客观评估以及ML/DL模型的训练或微调。最后，我们讨论了大语言模型（LLM）在自动化流程提取中的初步探索，以及该领域的潜在发展方向。
原文摘要 · Abstract (English)
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.
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