论文标题
迈向EHR问题的神经语义解析系统回答
Toward a Neural Semantic Parsing System for EHR Question Answering
论文作者
论文摘要
临床语义解析（SP）是从旨在从电子健康记录（EHRS）中检索信息的自然语言查询中确定确切信息需求（作为机器可靠的逻辑形式）的重要步骤。当前的临床SP方法主要基于传统的机器学习，需要手工构建词典。神经SP的最新进展表明，有望在没有太多人类努力的情况下建立强大而灵活的语义解析器。因此，在本文中，我们旨在系统地评估两个此类神经SP模型的EHR问题回答（QA）。我们发现，鉴于它们的易于应用和普遍性，这些高级神经模型在两个临床SP数据集上的性能很有希望。我们的错误分析表现出这些模型造成的常见错误类型，并有可能告知未来的研究，以改善EHR QA的神经SP模型的性能。
Clinical semantic parsing (SP) is an important step toward identifying the exact information need (as a machine-understandable logical form) from a natural language query aimed at retrieving information from electronic health records (EHRs). Current approaches to clinical SP are largely based on traditional machine learning and require hand-building a lexicon. The recent advancements in neural SP show a promise for building a robust and flexible semantic parser without much human effort. Thus, in this paper, we aim to systematically assess the performance of two such neural SP models for EHR question answering (QA). We found that the performance of these advanced neural models on two clinical SP datasets is promising given their ease of application and generalizability. Our error analysis surfaces the common types of errors made by these models and has the potential to inform future research into improving the performance of neural SP models for EHR QA.
