Beyond LLMs: A Linguistic Approach to Causal Graph Generation from Narrative Texts

• URL: http://arxiv.org/abs/2504.07459v1
• Date: Thu, 10 Apr 2025 05:09:07 GMT
• Title: Beyond LLMs: A Linguistic Approach to Causal Graph Generation from Narrative Texts
• Authors: Zehan Li, Ruhua Pan, Xinyu Pi,
• Abstract summary: We propose a novel framework for generating causal graphs from narrative texts.<n>We introduce an "Expert Index," comprising seven linguistically informed features, integrated into a Situation-Task-Action-Consequence (STAC) classification model.<n> Experiments on 100 narrative chapters and short stories demonstrate that our approach consistently outperforms GPT-4o and Claude 3.5 in causal graph quality.
• Score: 5.309459419455988
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel framework for generating causal graphs from narrative texts, bridging high-level causality and detailed event-specific relationships. Our method first extracts concise, agent-centered vertices using large language model (LLM)-based summarization. We introduce an "Expert Index," comprising seven linguistically informed features, integrated into a Situation-Task-Action-Consequence (STAC) classification model. This hybrid system, combining RoBERTa embeddings with the Expert Index, achieves superior precision in causal link identification compared to pure LLM-based approaches. Finally, a structured five-iteration prompting process refines and constructs connected causal graphs. Experiments on 100 narrative chapters and short stories demonstrate that our approach consistently outperforms GPT-4o and Claude 3.5 in causal graph quality, while maintaining readability. The open-source tool provides an interpretable, efficient solution for capturing nuanced causal chains in narratives.

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