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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 1, 2024.
Abstract: Sentence parsing is a fundamental step in the conversion of a text document into semantic graphs. In this research, novel phrase parsing techniques for semantic graph-based induction are presented, namely the ChatGPT-based and Hybrid Parser-based approaches. The performance of these two approaches in the context of inducing semantic networks from textual data is assessed through a comprehensive analysis in this study. The primary purpose is to enhance the construction of semantic graphs, specifically focusing on capturing detailed event descriptions and relationships within text. The research finds that the Hybrid Parser-Based approach exhibits a slight advantage in accuracy (acc hybrid = 0.87) compared to ChatGPT (acc GPT = 0.85) in sentence parsing tasks. Furthermore, the efficiency analysis reveals that ChatGPT’s response quality varies with different prompt sizes, while the Hybrid Parser-Based method consistently maintains an “excellent” response quality rating.
Walelign Tewabe and Laszlo Kovacs, “A Comparative Study of ChatGPT-based and Hybrid Parser-based Sentence Parsing Methods for Semantic Graph-based Induction” International Journal of Advanced Computer Science and Applications(IJACSA), 15(1), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01501117
@article{Tewabe2024,
title = {A Comparative Study of ChatGPT-based and Hybrid Parser-based Sentence Parsing Methods for Semantic Graph-based Induction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01501117},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01501117},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {1},
author = {Walelign Tewabe and Laszlo Kovacs}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.