## 3 Proposed Method
### Schema Definition and Annotation Guidelines
Similar improvements were observed for Re-DocRED [37] (7.527 to 9.150). These results highlight the importance of entity linking in enhancing the overall annotation accuracy, particularly in zero-shot learning scenarios, where model adaptability is crucial Table 4. Our model not only competed well with established models such as LLaMA2-7B, LLaMA2-13B [44], and Flan-T5-XXL [42], but also exhibited consistent competitive strength across the datasets. Notably, the inclusion of annotation and entity-linking processes significantly improved the performance of our model. This approach enables more accurate capturing of the contextual nuances and complex interactions between entities within documents. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Task-Specific SOTA** & \multicolumn{3}{c}{**Zero-Shot Evaluation**} \\ \cline{2-6}  & **PubMedBERT** & **GPT3** & **GPT-3.5-Turbo** & **Flan-T5-XXL** &
\begin{tabular}{c} **Proposed** \\ **Method** \\ \end{tabular} \\ \hline NCBI & 87.8 & 51.4 & 47.5 & 51.8 & 85.4 \\ BC5-disease & 85.6 & 73.0 & 67.2 & 54.7 & 87.3 \\ BC5-chem & 93.3 & 43.6 & 66.5 & 67.3 & 88.5 \\ BC2 GM & 84.5 & 41.1 & 47.7 & 42.4 & 67.2 \\ JNLPBA & 79.1 & 48.3 & 42.0 & 38.9 & 49.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance Comparison of Zero-Shot Information Extraction on Healthcare Datasets. Annotations help the model to develop a deeper understanding of specific cases within datasets, foster better connections between entities, and enhance its grasp of their interrelationships within documents. This method is particularly effective for zero-shot learning. Moreover, the entity-linking process strengthens the semantic connections and significantly improves the accuracy of the extracted information. This method is especially crucial for complex datasets containing various entities and relationships. By incorporating these additional steps, our proposed model achieved higher F1 scores than existing models. This highlights the importance of additional information in extracting complex relationships at the document level, thereby validating the suitability of our model for constructing depression-related knowledge graphs. The capability of the model to extract and link entities and their relationships within complex medical data accurately is essential for systematically mapping the interactions among various symptoms, diagnoses, and treatment methods associated with depression. Our approach integrates this information, providing medical professionals and researchers with rich resources to improve the understanding and management of depression. Therefore, the technological innovations and performance of our model are expected to contribute significantly to in-depth research on data-driven treatment strategies for depression. ## 5 Discussion

This study has investigated the effectiveness of entity linking combined with zero-shot information extraction using LLMs in the medical data domain. The findings highlight the critical role of entity linking in this context while also revealing some methodological limitations that offer opportunities for future research and applications in healthcare. * Performance evaluation and interpretation The results confirm that incorporating entity linking with LLMs significantly enhances zero-shot information extraction for medical data. The performance of our model surpassed that of traditional zero-shot approaches, demonstrating that these techniques can compete with conventional supervised learning methods. This is particularly valuable in healthcare, where data labeling is expensive and data privacy is paramount. * Importance of entity linking Entity linking plays a vital role in ensuring data consistency and boosting the model performance. In this study, this went beyond simple identification tasks. By significantly improving the overall data accuracy, entity linking underscores its importance in maintaining the integrity and usefulness of medical information systems.