Search Results for author:
Found 9 papers, 1 papers with code
Social Media Medical Concept Normalization using RoBERTa in Ontology Enriched Text Similarity Framework
It is the first work to leverage both concept description and synonyms to represent concepts in the form of retrofitted target concept vectors in text similarity framework based social media MCN.
Target Concept Guided Medical Concept Normalization in Noisy User-Generated Texts
In our model, we integrate a) target concept information in the form of target concept vectors generated by encoding target concept descriptions using SRoBERTa, state-of-the-art RoBERTa based sentence embedding model and b) domain lexicon knowledge by enriching target concept vectors with synonym relationship knowledge using retrofitting algorithm.
Medical Concept Normalization in User-Generated Texts by Learning Target Concept Embeddings
Second, we find cosine similarity between embeddings of input concept mention and all the target concepts.
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4
Large language models (LLMs) are a special class of pretrained language models obtained by scaling model size, pretraining corpus and computation.
AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing
These models are built on the top of transformers, self-supervised learning and transfer learning.
AMMU : A Survey of Transformer-based Biomedical Pretrained Language Models
We strongly believe there is a need for a survey paper that can provide a comprehensive survey of various transformer-based biomedical pretrained language models (BPLMs).
A Hybrid Approach to Measure Semantic Relatedness in Biomedical Concepts
The concept vectors generated using the Sentence BERT model based on SapBERT and retrofitted using UMLS-related concepts achieved the best results on all four datasets.
Want to Identify, Extract and Normalize Adverse Drug Reactions in Tweets? Use RoBERTa
Extracting ADR mentions is treated as sequence labeling and normalizing ADR mentions is treated as multi-class classification.
Medical Concept Normalization in User Generated Texts by Learning Target Concept Embeddings
Second, it finds cosine similarity between embeddings of input concept mention and all the target concepts.
