Time is an important concept in human-cognition, fundamental to a wide range of reasoning tasks in the clinical domain. Results of the Clinical TempEval 2016 challenge, a set of shared tasks that evaluate temporal information extraction systems in the clinical domain, indicate that current state-of-the-art systems do well in solving event and time expression identification but perform poorly in temporal relation extraction. This study aims to identify and analyze the reason(s) for this uneven performance. It adapts a general domain tree-based bidirectional long short-term memory recurrent neural network model for semantic relation extraction to the task of temporal relation extraction in the clinical domain, and tests the system in a binary and multi-class classification setting by experimenting with general and in-domain word embeddings. Its results outperform the best Clinical TempEval 2016 system and the current stateof-the-art model. However, there is still a significant gap between the system and human performance. Consequently, this study delivers a deep analysis of the results, identifying a high incidence of nouns as events and class overlapping as posing major challenges in this task.

Reading comprehension (RC) through question answering is a useful method for evaluating if a reader understands a text. Standard accuracy metrics are used for evaluation, where a high accuracy is taken as indicative of a good understanding. However, literature in quality learning suggests that task performance should also be evaluated on the undergone process to answer. The Question-Answer Relationship (QAR) is one of the strategies for evaluating a reader's understanding based on their ability to select different sources of information depending on the question type. We propose the creation of a dataset to learn the QAR strategy with weak supervision. We expect to complement current work on reading comprehension by introducing a new setup for evaluation.