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

Fuente: arXiv

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Detalles Bibliográficos
Autores principales:	Van Woensel, William, Motie, Soroor
Formato:	Preprint
Publicado:	2024
Materias:	Computation and Language
Acceso en línea:	Acceder al recurso
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author	Van Woensel, William Motie, Soroor
author_facet	Van Woensel, William Motie, Soroor
contents	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.
format	Preprint
id	arxiv_https___arxiv_org_abs_2409_13738
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	NLP4PBM: A Systematic Review on Process Extraction using Natural Language Processing with Rule-based, Machine and Deep Learning Methods Van Woensel, William Motie, Soroor Computation and Language 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.
title	NLP4PBM: A Systematic Review on Process Extraction using Natural Language Processing with Rule-based, Machine and Deep Learning Methods
topic	Computation and Language
url	https://arxiv.org/abs/2409.13738

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