TL;DR
This study introduces E6, a unique method that combines structured prediction utilizing Conditional Random Fields with BIOES labeling, domain-adapted language models (PubMedBERT), and a new span-level contrastive loss that specifically addresses boundary detection problems.
The proposed AGPNer integrates a heterogeneous dependency fusion encoder, which reconstructs masked entities to enhance token representations and fine-tunes a hybrid dependency modeling block to learn domain-specific patterns in medical texts; an imbalance-adaptive span decoder, which decouples entity and non-entity sp...
How should natural language processing models be selected and adapted for global health literature in environments where annotated data and computational resources are limited? This thesis investigates these challenges through experiments on semantic tag discovery, named entity recognition (NER), and multi-label topic...
A new embedding adaptation procedure is defined whose fine-tuning approach is guided by a novel "Disease-Overlap" similarity measure, which prioritizes clinical co-occurrence of phenotypes over taxonomic distance, and optimizes the embedding space using AnglE Loss to mitigate gradient saturation.
Biomedical named entity recognition (NER) and relation extraction (RE) remain challenging because biomedical texts contain ambiguous abbreviations, complex entity boundaries, domain-specific terminology, and implicit relations. This study proposes a prompt-enhanced and QLoRA-adapted large language model framework for b...
BELXTR is presented, a novel embedding model based on the multi-vector (a.k.a. late interaction) architecture, which allows to leverage token-level matching information in biomedical entity linking by integrating an existing task-specific training objective and exploring active query expansion.
Findings demonstrate that compact models can achieve strong biomedical classification performance through KD under compatible teacher–student pairings, while also highlighting that KD effectiveness varies substantially depending on the specific model combination.
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