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Recent supervised models for event coding vastly outperform pattern-matchingmethods. However, their reliance solely on new annotations disregards the vastknowledge within expert databases, hindering their applicability tofine-grained classification. To address these limitations, we explore zero-shotapproaches for political event ontology relation classification, by leveragingknowledge from established annotation codebooks. Our study encompasses bothChatGPT and a novel natural language inference (NLI) based approach named ZSP.ZSP adopts a tree-query framework that deconstructs the task into context,modality, and class disambiguation levels. This framework improvesinterpretability, efficiency, and adaptability to schema changes. By conductingextensive experiments on our newly curated datasets, we pinpoint theinstability issues within ChatGPT and highlight the superior performance ofZSP. ZSP achieves an impressive 40% improvement in F1 score for fine-grainedRootcode classification. ZSP demonstrates competitive performance compared tosupervised BERT models, positioning it as a valuable tool for event recordvalidation and ontology development. Our work underscores the potential ofleveraging transfer learning and existing expertise to enhance the efficiencyand scalability of research in the field.

Synthesizing Political Zero-Shot Relation Classification via Codebook Knowledge, NLI, and ChatGPT