## 3 Proposed Method
### Schema Definition and Annotation Guidelines
Our study represents significant advancements in zero-shot information extraction. Unlike traditional methods, our proposed method demonstrates the ability to make accurate predictions without training data. The proposed method achieves significant performance improvements on the NCBI Disease [39], BC5-Disease, and BC5-Chemical [38] datasets, surpassing existing zero-shot models such as GPT-3 In-context learning [1], GPT-3.5-Turbo, and Flan-T5-XXL [42]. These results suggest that our research has the potential to significantly expand the boundaries of zero-shot learning in the medical domain. Additionally, compared to traditional supervised learning models like PubMed-BERT [43], the proposed method maintains competitive performance with significantly fewer data points. PubMedBERT is a widely used pre-trained model in biomedical text analysis and is directly relevant to our research objectives. By selecting such domain-specific models, we demonstrate the advantages and practicality of our zero-shot approach. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Dataset Name** & **Source** & **Document Count** & **Primary Entity Type** & **Entity Count** \\ \hline Biomedical Dataset & BC5-Chemical [38] & PubMed Abstracts & 1500 & Chemicals (Drugs) & N/A \\ BC5-Disease [38] & PubMed Abstracts & 1500 & Diseases & N/A \\ NCBI-Disease [39] & PubMed Abstracts & 793 & Diseases & 6,892 \\ BC2 GM [40] & PubMed Abstracts & N/A & Alternative Gene & N/A \\ Products & JNLPBA [41] & PubMed Abstracts & N/A & Proteins, DNA, RNA, Cell Lines, Cell Types & N/A \\ \hline Document-level Information Extraction & DocRED [36] & Wikipedia & 9228 & Relationships & 57,263 Triples \\ Re-DocRED [37] & Wikipedia & 11,854 & Relationships & 70,608 Triples \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of statistics for all datasets. The proposed method achieves an F1 score of 85.4% on the NCBI Disease dataset, closely matching PubMedBERT's 87.8%. On the BC5-Disease dataset, the proposed method scores 87.3%, outperforming PubMedBERT (85.6%). In the BC5-Chemical dataset, the proposed method scores 88.5%, significantly higher than GPT-3 (43.6%), GPT-3.5-Turbo (66.5%), and Flan-T5-XXL (67.3%). Particularly, on the BC2 GM dataset, the proposed method achieves an F1 score of 67.2%, surpassing GPT-3 (41.1%), GPT-3.5-Turbo (47.7%), and Flan-T5-XXL (42.4%). Lastly, on the JNLPBA dataset, the proposed method scores 49.7%, demonstrating higher performance compared to other zero-shot models. These results clearly indicate that our proposed zero-shot method can achieve results comparable to state-of-the-art supervised learning models while significantly outperforming other zero-shot models. The robust performance across diverse datasets illustrates the model's adaptability and effectiveness, supporting its applicability in various real-world scenarios. Our study provides compelling evidence of the robustness of the proposed method. The results shown in the table highlight the competitive performance of our zero-shot approach compared to traditional supervised learning models and other zero-shot models across different medical datasets. Specifically, the zero-shot learning model demonstrates F1 scores comparable to those of supervised models trained on specific datasets. This underscores the model's ability to generalize and perform well even without fine-tuning on the target datasets. The strong performance under the constraints of zero-shot learning further proves the strength and reliability of our approach in real-world applications. #### 4.2.2 Zero-Shot Relationship Extraction at the Document Level

Our investigation of document-level relationship extraction demonstrates the potential of zero-shot approaches in advancing this field. The relevant results are presented in Table 3. Implementing entity linking significantly improved the performance compared to not using it. For example, on the DocRED dataset [36], the F1 score increased from 7.803 (without entity linking) to 9.844 (with entity linking).