A Lightweight Hybrid Transformer-CRF Architecture for Multi-Type Bangla Medical Entity Recognition
arXiv:2605.25463v1 Announce Type: new Abstract: MedER refers to the identification of medical entities. It is crucial for extracting structured clinical information from unstructured medical text. Many existing systems rely on transformer-based models, which are computationally expensive and difficult to deploy in resource-constrained environments. Furthermore, earlier works often use relaxed evaluation metrics that artificially inflate performance by rewarding correct prediction of dominant "Outside" (O) tokens. In this paper, we propose a lightweight Medical Entity Recognition (MedER) framew
The proliferation of AI applications necessitates more efficient and resource-friendly models, especially for languages with less English-centric tooling and data. This research addresses the computational burden of existing transformer-based models.
This development could enable broader and more practical deployment of medical entity recognition in resource-constrained environments and non-English languages, expanding AI accessibility and utility in healthcare. It offers a solution to the computational intensity that limits current transformer models.
The proposed lightweight hybridization offers a pathway to more efficient and accurate medical entity recognition for under-resourced languages like Bangla, potentially lowering barriers to entry for AI in medical text analysis. This marks a move towards optimizing existing AI techniques for practical deployment.
- · Healthcare providers in developing regions
- · NLP researchers focused on low-resource languages
- · AI developers focused on efficiency
- · Bangla-speaking medical professionals
- · Developers of computationally expensive models
- · Providers of models requiring significant compute infrastructure
Improved extraction of structured clinical information from Bangla medical texts becomes more feasible.
Enhanced medical research and diagnostics in Bangla-speaking regions due to better data analysis capabilities.
Potential for similar lightweight architectures to be developed for other non-English, low-resource medical NLP tasks globally.
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