While Word Sense Disambiguation (WSD) is a powerful tool for improving the quality of data used in bias detection, it does have several limitations that need to be addressed:
Ambiguity in Context: WSD relies heavily on context to determine the correct meaning of a word. In medical texts, the context can be complex and nuanced, leading to potential misclassification. For example, the term "white" could refer to race or to anatomical structures (e.g., "white matter"). To address this, a more sophisticated context analysis could be implemented, possibly using contextual embeddings from advanced language models that capture the surrounding text more effectively.
Limited Training Data: The effectiveness of WSD models is contingent on the availability of high-quality annotated training data. In the medical domain, such data may be scarce or biased itself. To mitigate this, researchers can employ data augmentation techniques, such as generating synthetic examples using language models like ChatGPT, to enhance the training dataset.
Domain-Specific Challenges: Medical terminology often includes jargon and abbreviations that may not be well-represented in general WSD models. Developing domain-specific WSD models trained on medical corpora can help improve accuracy. Collaborating with medical professionals to refine the model's understanding of context-specific terms can also enhance performance.
Computational Complexity: Implementing WSD can introduce additional computational overhead, especially when processing large datasets. Optimizing the WSD algorithms for efficiency and exploring parallel processing techniques can help alleviate this issue.
Evaluation of WSD Performance: The performance of WSD models needs to be rigorously evaluated to ensure they are effectively improving bias detection. Establishing clear benchmarks and conducting comparative studies with and without WSD can provide insights into its impact.
By addressing these limitations, the application of WSD in bias detection can be significantly enhanced, leading to more accurate identification of biases in medical texts.