## 3 Proposed Method
### Schema Definition and Annotation Guidelines
In addition, the DocRED [36] and Re-DocRED datasets [37] were used for document-level information extraction, with a specific focus on extracting and systematizing depression-related information through complex relationship extraction. #### 4.1.1 BioCreative Datasets

* BC5-Chemical and BC5-Disease [38]: Derived from the BioCreative V chemical-disease relation corpus, these datasets focus on exploring interactions between drugs and diseases. Each dataset includes 1500 PubMed abstracts (evenly split) for training, development, and testing. We used a preprocessed version by [PERSON] et al., focusing on the named entity recognition (NER) of chemicals and diseases without relationship labels. * NCBI Disease [39]: Provided by the National Center for Biotechnology Information (NCBI), this dataset includes 793 PubMed abstracts with 6,892 disease mentions linked to 790 unique disease entities. We used a preprocessed version by [PERSON] et al. for training, development, and testing splits. * BC2 GM [40]: Originating from the BioCreative II gene mention corpus, this dataset consists of sentences from PubMed abstracts with manually tagged genes and alternative gene entities. Our study focused on gene entity annotations using a version of the dataset separated for development by [PERSON] et al. * JNLPBA [41]: Designed for applications in molecular biology, this dataset focuses on NER for entity types such as proteins, DNA, RNA, cell lines, and cell types. We focused on entity mention detection without differentiating between entity types, using the same splits as those of [PERSON] et al. #### 4.1.2 Document-Level Relationship Extraction Datasets

* DocRED [36]: This dataset was constructed using Wikipedia and Wikidata for document-level relationship extraction. It contains 9228 documents and 57,263 relationship triples, covering 96 predefined relationship types. DocRED was used to evaluate the ability to extract relationships from complex texts spanning multiple sentences. * Re-DocRED [37]: Re-DocRED, which is an expanded version of DocRED, includes additional positive cases (11,854 documents and 70,608 relationship triples) and in \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Subject** & **Object** & **Relation** \\ \hline
5 & Major depressive disorder & Decreased energy levels & manifests as \\... &... &... &... \\
483 & Unspecified depressive disorder & Appetite change & lasts \\
484 & Unspecified depressive disorder & Weight change & lasts \\
485 & Unspecified depressive disorder & Sexual interest or desire & lasts \\
486 & Unspecified depressive disorder & Sleep disturbance & includes \\
487 & Unspecified depressive disorder & Psychomotor changes & includes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._corporates relationship types and scenarios that are not addressed by DocRED. This dataset is useful for research aimed at identifying diverse and in-depth relationship patterns within documents. Table 2 summarizes the statistics for all of the datasets. This structured approach allowed a comprehensive evaluation of the proposed zero-shot information extraction methodology, particularly in the context of mental health data. This ensured a robust assessment and valuable insights into its applicability. ### Experimental Results

This section presents the findings of the experiments evaluating our zero-shot information extraction approach. The F1 score is calculated as follows and is expressed as a percentage by multiplying the final value by 100:

\[\text{F1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}}\times 100\%\]

This approach makes the interpretation of the results clearer and facilitates easier comparison of small differences. #### 4.2.1 Zero-Shot Information Extraction on Healthcare Datasets

This section presents the evaluation results of our proposed zero-shot information extraction method compared to traditional supervised learning models and other zero-shot models. The performance comparison is detailed in the table.