A Comparative Study of ChatGPT-based and Hybrid Parser-based Sentence Parsing Methods for Semantic Graph-based Induction

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 graphbased 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.