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Found 10 papers, 8 papers with code
KU_ED at SocialDisNER: Extracting Disease Mentions in Tweets Written in Spanish
This paper describes our system developed for the Social Media Mining for Health (SMM4H) 2022 SocialDisNER task.
Biomedical NER for the Enterprise with Distillated BERN2 and the Kazu Framework
In order to assist the drug discovery/development process, pharmaceutical companies often apply biomedical NER and linking techniques over internal and public corpora.
Improving Tagging Consistency and Entity Coverage for Chemical Identification in Full-text Articles
This paper is a technical report on our system submitted to the chemical identification task of the BioCreative VII Track 2 challenge.
Sequence tagging for biomedical extractive question answering
Following general domain EQA models, current biomedical EQA (BioEQA) models utilize the single-span extraction setting with post-processing steps.
Transferability of Natural Language Inference to Biomedical Question Answering
We observe that BioBERT trained on the NLI dataset obtains better performance on Yes/No (+5. 59%), Factoid (+0. 53%), List type (+13. 58%) questions compared to performance obtained in a previous challenge (BioASQ 7B Phase B).
Answering Questions on COVID-19 in Real-Time
The recent outbreak of the novel coronavirus is wreaking havoc on the world and researchers are struggling to effectively combat it.
Learning by Semantic Similarity Makes Abstractive Summarization Better
By harnessing pre-trained language models, summarization models had rapid progress recently.
Pre-trained Language Model for Biomedical Question Answering
The recent success of question answering systems is largely attributed to pre-trained language models.
BioBERT: a pre-trained biomedical language representation model for biomedical text mining
Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows.
CollaboNet: collaboration of deep neural networks for biomedical named entity recognition
Our model has successfully reduced the number of misclassified entities and improved the performance by leveraging multiple datasets annotated for different entity types.
Ranked #16 on
Named Entity Recognition (NER)
on BC5CDR
