1 citations · 1 across the 4 of their papers we have counts for
5 papers
Improving Pre-trained Language Model Sensitivity via Mask Specific losses: A case study on Biomedical NER
Micheal Abaho, Danushka Bollegala, Gary Leeming +2
Adapting language models (LMs) to novel domains is often achieved through fine-tuning a pre-trained LM (PLM) on domain-specific data. Fine-tuning introduces new knowledge into an L…
Select and Augment: Enhanced Dense Retrieval Knowledge Graph Augmentation
Micheal Abaho, Yousef H. Alfaifi
Injecting textual information into knowledge graph (KG) entity representations has been a worthwhile expedition in terms of improving performance in KG oriented tasks within the NL…
Position-based Prompting for Health Outcome Generation
M. Abaho, D. Bollegala, P. Williamson +1
Probing Pre-trained Language Models (PLMs) using prompts has indirectly implied that language models (LMs) can be treated as knowledge bases. To this end, this phenomena has been e…
Assessment of contextualised representations in detecting outcome phrases in clinical trials
Micheal Abaho, Danushka Bollegala, Paula R Williamson +1
Automating the recognition of outcomes reported in clinical trials using machine learning has a huge potential of speeding up access to evidence necessary in healthcare decision-ma…
Detect and Classify -- Joint Span Detection and Classification for Health Outcomes
Michael Abaho, Danushka Bollegala, Paula Williamson +1
A health outcome is a measurement or an observation used to capture and assess the effect of a treatment. Automatic detection of health outcomes from text would undoubtedly speed u…