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Found 5 papers, 1 papers with code
Detect and Classify – Joint Span Detection and Classification for Health Outcomes
To address this, we propose a method that uses both word-level and sentence-level information to simultaneously perform outcome span detection and outcome type classification.
Position-based Prompting for Health Outcome Generation
Probing factual knowledge in Pre-trained Language Models (PLMs) using prompts has indirectly implied that language models (LMs) can be treated as knowledge bases.
Improving Pre-trained Language Model Sensitivity via Mask Specific losses: A case study on Biomedical NER
To address insensitive fine-tuning, we propose Mask Specific Language Modeling (MSLM), an approach that efficiently acquires target domain knowledge by appropriately weighting the importance of domain-specific terms (DS-terms) during fine-tuning.
Select and Augment: Enhanced Dense Retrieval Knowledge Graph Augmentation
Instead of using a single text description (which would not sufficiently represent an entity because of the inherent lexical ambiguity of text), we propose a multi-task framework that jointly selects a set of text descriptions relevant to KG entities as well as align or augment KG embeddings with text descriptions.
Assessment of contextualised representations in detecting outcome phrases in clinical trials
We reach a consensus on which contextualized representations are best suited for detecting outcomes from clinical-trial abstracts.
