Potential Limitations:
Limited Contextual Understanding: Chunk-based retrieval may struggle with capturing the full context of complex biomedical text, leading to potential information loss or misinterpretation of relationships between entities.
Dependency on Chunking Algorithm: The effectiveness of chunk-based retrieval heavily relies on the accuracy of the chunking algorithm used. Inaccurate chunking can result in irrelevant or incomplete information retrieval.
Scalability Issues: Handling diverse and unstructured biomedical text may pose scalability challenges for chunk-based retrieval, especially when dealing with large volumes of data or complex relationships between entities.
Improvements:
Dynamic Chunking: Implementing dynamic chunking strategies that adapt to the specific characteristics of the text can enhance the retrieval process. This approach can involve variable chunk sizes based on the complexity of the text or the relationships being analyzed.
Semantic Similarity Matching: Integrating semantic similarity measures into the retrieval process can improve the relevance of retrieved chunks. Utilizing techniques like word embeddings or contextual embeddings can enhance the understanding of text semantics and improve chunk selection.
Multi-level Chunking: Incorporating multi-level chunking, where text is segmented at different granularities, can capture diverse information hierarchically. This approach allows for a more comprehensive representation of the text and facilitates handling diverse and unstructured biomedical content.
Ensemble Methods: Combining multiple retrieval strategies, such as chunk-based retrieval with keyword-based or entity-based retrieval, can mitigate the limitations of individual approaches. Ensemble methods can improve the robustness and accuracy of information retrieval in diverse biomedical text settings.