Procedural Text Mining with Large Language Models-Reference-Cited by-同舟云学术

Procedural Text Mining with Large Language Models

Published:2023-12-05 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 12th Knowledge Capture Conference 2023
language:
Short-container-title:

Author:

Rula Anisa¹^ORCID,D'Souza Jennifer²^ORCID

Affiliation:

1. Information Engineering, University of Brescia, Italy

2. TIB Leibniz Information Centre for Science and Technology, Germany

Funder

German BMBF project SCINEXT (ID 01lS22070)

MICS (Made in Italy ? Circular and Sustainable) Extended Partnership and received funding from Next-GenerationEU (Italian PNRR ? M4 C2, Invest 1.3 ? D.D. 1551.11-10-2022)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3587259.3627572

Reference22 articles.

1. Patrizio Bellan, Mauro Dragoni, and Chiara Ghidini. 2021. Process Extraction from Text: state of the art and challenges for the future. CoRR abs/2110.03754 (2021).

2. Extracting Business Process Entities and Relations from Text Using Pre-trained Language Models and In-Context Learning

3. Semantic modeling and analysis of complex data-aware processes and their executions

4. Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, 2022. Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416 (2022).

5. Transformation from human-readable documents and archives in arc welding domain to machine-interpretable data

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