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Found 5 papers, 1 papers with code
BiTeM at WNUT 2020 Shared Task-1: Named Entity Recognition over Wet Lab Protocols using an Ensemble of Contextual Language Models
Recent improvements in machine-reading technologies attracted much attention to automation problems and their possibilities.
BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition
We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese.
Ranked #1 on
Named Entity Recognition (NER)
on SemClinBr
Classification of hierarchical text using geometric deep learning: the case of clinical trials corpus
We consider the hierarchical representation of documents as graphs and use geometric deep learning to classify them into different categories.
Named entity recognition in chemical patents using ensemble of contextual language models
The results show that ensemble of contextualized language models can provide an effective method to extract information from chemical patents.
Contextualized French Language Models for Biomedical Named Entity Recognition
Our best approach achieved an F1 -measure of 66{\%} for symptoms and signs, and pathology categories, being top 1 for subtask 1.
