CORE-KG reduces node duplication by 33.28% and legal noise by 38.37% versus a GraphRAG baseline on 20 human smuggling court cases, through type-aware LLM coreference resolution and domain-filtered extraction prompts.
Coreference Resolution for Vietnamese Narrative Texts
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abstract
Coreference resolution is a vital task in natural language processing (NLP) that involves identifying and linking different expressions in a text that refer to the same entity. This task is particularly challenging for Vietnamese, a low-resource language with limited annotated datasets. To address these challenges, we developed a comprehensive annotated dataset using narrative texts from VnExpress, a widely-read Vietnamese online news platform. We established detailed guidelines for annotating entities, focusing on ensuring consistency and accuracy. Additionally, we evaluated the performance of large language models (LLMs), specifically GPT-3.5-Turbo and GPT-4, on this dataset. Our results demonstrate that GPT-4 significantly outperforms GPT-3.5-Turbo in terms of both accuracy and response consistency, making it a more reliable tool for coreference resolution in Vietnamese.
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CORE-KG: An LLM-Driven Knowledge Graph Construction Framework for Human Smuggling Networks
CORE-KG reduces node duplication by 33.28% and legal noise by 38.37% versus a GraphRAG baseline on 20 human smuggling court cases, through type-aware LLM coreference resolution and domain-filtered extraction prompts.