提出临床文本三任务联合抽取新方法，性能显著优于传统流程
A Joint Neural Baseline for Concept, Assertion, and Relation Extraction from Clinical Text
- 设计端到端联合框架，统一处理概念、断言、关系三类任务
- 在多个嵌入技术下，三项任务F1均提升，最高达+3.1
- 为临床信息抽取提供可复现的强基线，适合后续研究参考
临床信息抽取（如2010 i2b2/VA挑战赛）通常包含概念识别、断言分类和关系抽取。在临床领域，多阶段任务的联合建模仍属未充分探索方向。现有独立任务设置（各阶段依赖参考输入）导致联合模型难以与传统流水线方法直接比较。为此，我们定义了一种新的联合任务设置，并提出一种新型端到端系统，用于联合优化三个阶段任务。我们通过实验评估了该方案与流水线基线在不同嵌入技术（词向量、上下文向量、领域内上下文向量）下的联合表现。结果表明，所提联合系统在概念、断言、关系任务上的F1值分别较流水线基线提升+0.3、+1.4、+3.1。本工作弥合了联合方法与临床信息抽取之间的差距，提出的方案可作为未来研究的强基线。代码已公开。
原文摘要 · Abstract (English)
Clinical information extraction (e.g., 2010 i2b2/VA challenge) usually presents tasks of concept recognition, assertion classification, and relation extraction. Jointly modeling the multi-stage tasks in the clinical domain is an underexplored topic. The existing independent task setting (reference inputs given in each stage) makes the joint models not directly comparable to the existing pipeline work. To address these issues, we define a joint task setting and propose a novel end-to-end system to jointly optimize three-stage tasks. We empirically investigate the joint evaluation of our proposal and the pipeline baseline with various embedding techniques: word, contextual, and in-domain contextual embeddings. The proposed joint system substantially outperforms the pipeline baseline by +0.3, +1.4, +3.1 for the concept, assertion, and relation F1. This work bridges joint approaches and clinical information extraction. The proposed approach could serve as a strong joint baseline for future research. The code is publicly available.
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