## 3 Proposed Method
### Schema Definition and Annotation Guidelines
By providing detailed annotation guidelines, we ensure that the GoLLIE model can recognize and process information accurately and consistently. Our research provides an annotation guideline for entity definition as follows: \"Represents an entity in a document, which could be a person, location, organization, or any other named object. The 'name' attribute holds the canonical name of the entity as identified in the document text\". This approach enhances data consistency and reliability, leading to more precise and reliable data analysis. In summary, integrating schema definitions and annotation guidelines as prompts is essential for leveraging the full capabilities of the GoLLIE model, ensuring superior performance in zero-shot information extraction tasks. ### Information Extraction and Entity Linking

Information Extraction: This stage also utilizes the gpt-3.5-turbo-instruct model [24], leveraging its NLP capabilities to identify key information and entities within the documents. The model excels at recognizing meaningful patterns and structures in text, particularly within specialized domains such as mental health text, ensuring high accuracy in information extraction [30]. This is achieved as follows:

* Context-aware recognition: The model analyzes the surrounding text to determine the precise scope and types of entities. For instance, it can distinguish whether \"depression\" refers to a clinical condition or a general emotional state [31]. * Entity classification: The extracted information is categorized based on predefined classes (e.g., symptoms, diagnoses, and treatments) aligned with the established schema. Entity Linking: Entity linking is crucial to guarantee the quality and accuracy of depression-related data annotations. This study employs the NLP capabilities of the gpt-3.5-turbo-instruct model to perform entity-linking tasks effectively. The model identifies relevant entities within the data and accurately maps their relationships. These high-quality annotations, termed \"gold annotations\", are then fed into the guideline-based model described in the following section [32]. This step is instrumental in enabling more refined data analysis and information extraction, which are essential for the efficient processing of complex mental health data. The entity linking process consists of the following key steps:

1. Symptom Integration and Standardization: Similar or redundant symptoms are integrated and standardized to ensure consistency. This step addresses the issue of varied expressions for the same concept, converting data into a unified format. 2. Triple Construction: New triples are constructed using the standardized symptoms and durations. This process generates new triples based on integrated symptoms, maintaining data consistency. 3. Entity Linking: The constructed triples are linked to standardized terms, ensuring data consistency and reliability. Entity linking connects the extracted information with standardized terms, enhancing data consistency and improving interoperability among data collected from various sources. To illustrate the entity linking process, we present a case study on the diagnosis of Major Depressive Disorder (MDD). Case Study: Entity Linking for Major Depressive Disorder

This case study demonstrates the entity linking process applied to the diagnosis of Major Depressive Disorder (MDD). The raw text used is as follows:

\"A diagnosis of Major Depressive Disorder requires that the patient experiences profound sadness or a loss of interest or pleasure most of the time for at least two weeks\". First, the gpt-3.5-turbo-instruct model is used to extract information from the raw text.