Olex Amy L, McInnes Bridget T
C. Kenneth and Diane Wright Center for Clinical and Translational Research, Virginia Commonwealth University, Richmond, VA, United States.
Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States.
Front Res Metr Anal. 2022 Oct 24;7:1001266. doi: 10.3389/frma.2022.1001266. eCollection 2022.
Temporal expression recognition and normalization (TERN) is the foundation for all higher-level temporal reasoning tasks in natural language processing, such as timeline extraction, so it must be performed well to limit error propagation. Achieving new heights in state-of-the-art performance for TERN in clinical texts requires knowledge of where current systems struggle. In this work, we summarize the results of a detailed error analysis for three top performing state-of-the-art TERN systems that participated in the 2012 i2b2 Clinical Temporal Relation Challenge, and compare our own home-grown system Chrono to identify specific areas in need of improvement. Performance metrics and an error analysis reveal that all systems have reduced performance in normalization of relative temporal expressions, specifically in disambiguating temporal types and in the identification of the correct anchor time. To address the issue of temporal disambiguation we developed and integrated a module into Chrono that utilizes temporally fine-tuned contextual word embeddings to disambiguate relative temporal expressions. Chrono now achieves state-of-the-art performance for temporal disambiguation of relative temporal expressions in clinical text, and is the only TERN system to output dual annotations into both TimeML and SCATE schemes.
时间表达识别与归一化（TERN）是自然语言处理中所有高级时间推理任务（如时间线提取）的基础，因此必须做好以限制错误传播。要在临床文本中的TERN方面达到最先进性能的新高度，需要了解当前系统的难点所在。在这项工作中，我们总结了对参加2012年i2b2临床时间关系挑战赛的三个性能最佳的最先进TERN系统进行详细错误分析的结果，并将我们自己开发的Chrono系统与之比较，以确定需要改进的具体领域。性能指标和错误分析表明，所有系统在相对时间表达的归一化方面性能都有所下降，特别是在消除时间类型的歧义以及确定正确的锚定时间方面。为了解决时间歧义问题，我们开发并将一个模块集成到Chrono中，该模块利用经过时间微调的上下文词嵌入来消除相对时间表达的歧义。Chrono现在在临床文本中相对时间表达的时间歧义消除方面达到了最先进的性能，并且是唯一能同时输出TimeML和SCATE方案双重注释的TERN系统。
