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Found 6 papers, 3 papers with code
Scalable Few-Shot Learning of Robust Biomedical Name Representations
Recent research on robust representations of biomedical names has focused on modeling large amounts of fine-grained conceptual distinctions using complex neural encoders.
Conceptual Grounding Constraints for Truly Robust Biomedical Name Representations
Effective representation of biomedical names for downstream NLP tasks requires the encoding of both lexical as well as domain-specific semantic information.
Integrating Higher-Level Semantics into Robust Biomedical Name Representations
It has not yet been empirically confirmed that training biomedical name encoders on fine-grained distinctions automatically leads to bottom-up encoding of such higher-level semantics.
Mapping probability word problems to executable representations
While solving math word problems automatically has received considerable attention in the NLP community, few works have addressed probability word problems specifically.
Are we there yet? Exploring clinical domain knowledge of BERT models
We explore whether state-of-the-art BERT models encode sufficient domain knowledge to correctly perform domain-specific inference.
Contextual explanation rules for neural clinical classifiers
For evaluation of explanations, we create a synthetic sepsis-identification dataset, as well as apply our technique on additional clinical and sentiment analysis datasets.
