Zhijing Li


2017

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XJNLP at SemEval-2017 Task 12: Clinical temporal information ex-traction with a Hybrid Model
Yu Long | Zhijing Li | Xuan Wang | Chen Li
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

Temporality is crucial in understanding the course of clinical events from a patient’s electronic health recordsand temporal processing is becoming more and more important for improving access to content. SemEval 2017 Task 12 (Clinical TempEval) addressed this challenge using the THYME corpus, a corpus of clinical narratives annotated with a schema based on TimeML2 guidelines. We developed and evaluated approaches for: extraction of temporal expressions (TIMEX3) and EVENTs; EVENT attributes; document-time relations. Our approach is a hybrid model which is based on rule based methods, semi-supervised learning, and semantic features with addition of manually crafted rules.