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Clinical semantic parsing (SP) is an important step toward identifying theexact information need (as a machine-understandable logical form) from anatural language query aimed at retrieving information from electronic healthrecords (EHRs). Current approaches to clinical SP are largely based ontraditional machine learning and require hand-building a lexicon. The recentadvancements in neural SP show a promise for building a robust and flexiblesemantic parser without much human effort. Thus, in this paper, we aim tosystematically assess the performance of two such neural SP models for EHRquestion answering (QA). We found that the performance of these advanced neuralmodels on two clinical SP datasets is promising given their ease of applicationand generalizability. Our error analysis surfaces the common types of errorsmade by these models and has the potential to inform future research intoimproving the performance of neural SP models for EHR QA.

Toward a Neural Semantic Parsing System for EHR Question Answering