Search Results for author:
Found 6 papers, 4 papers with code
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.
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.
Unsupervised Context-Sensitive Spelling Correction of English and Dutch Clinical Free-Text with Word and Character N-Gram Embeddings
We present an unsupervised context-sensitive spelling correction method for clinical free-text that uses word and character n-gram embeddings.
Unsupervised Context-Sensitive Spelling Correction of Clinical Free-Text with Word and Character N-Gram Embeddings
We present an unsupervised context-sensitive spelling correction method for clinical free-text that uses word and character n-gram embeddings.
