We submitted two systems to the SemEval-2016 Task 12: Clinical TempEval
challenge, participating in Phase 1, where we identified text spans of time and
event expressions in clinical notes and Phase 2, where we predicted a relation
between an event and its parent document creation time.
For temporal entity extraction, we find that a joint inference-based approach
using structured prediction outperforms a vanilla recurrent neural network that
incorporates word embeddings trained on a variety of large clinical document
sets. For document creation time relations, we find that a combination of date
canonicalization and distant supervision rules for predicting relations on both
events and time expressions improves classification, though gains are limited,
likely due to the small scale of training data.

我们提交了两个系统参加 SemEval-2016 任务 12：临床 TempEval 挑战赛，我们在第 1 阶段参加，识别了临床笔记中的时间和事件表达式的文本跨度，第 2 阶段中，我们预测了事件与其父文档创建时间之间的关系。在时间实体提取方面，我们发现使用基于结构化预测的联合推断方法胜过仅包含针对大量临床文件集训练的词嵌入的普通递归神经网络。对于文档创建时间关系，我们发现将日期规范化和远程监督规则相结合，以预测事件和时间表达式上的关系，可以提高分类功效，尽管收益有限，可能是由于训练数据规模较小所致。