This paper investigates a machine learning approach for temporally ordering and anchoring events in natural language texts. To address data sparseness, we used temporal reasoning as an oversampling method to dramatically expand the amount of training data, resulting in predictive accuracy on link labeling as high as 93% using a Maximum Entropy classifier on human annotated data. This method compared favorably against a series of increasingly sophisticated baselines involving expansion of rules derived from human intuitions.
This study proposes a system to automatically analyze clinical temporal events in a fine-grained level in SemEval-2017. Support vector machine (SVM) and conditional random field (CRF) were implemented in our system for different subtasks, including detecting clinical relevant events and time expression, determining their attributes , and identifying their relations with each other within the document. Domain adaptation was the main challenge this year. Unified Medical Language System was consulted to generalize events specific to each domain. The results showed our system's capability of domain adaptation.
Clinical TempEval 2015 brought the temporal information extraction tasks of past TempEval campaigns to the clinical domain. Nine sub-tasks were included, covering problems in time expression identification, event expression identification and temporal relation identification. Participant systems were trained and evaluated on a corpus of clinical notes and pathology reports from the Mayo Clinic, annotated with an extension of TimeML for the clinical domain. Three teams submitted a total of 13 system runs, with the best systems achieving near-human performance on identifying events and times, but with a large performance gap still remaining for temporal relations.
We present an overview of TARSQI, a modular system for automatic temporal annotation that adds time expressions, events and temporal relations to news texts.
TempEval is a framework for evaluating systems that automatically annotate texts with temporal relations. It was created in the context of the SemEval 2007 workshop and uses the TimeML annotation language. The evaluation consists of three subtasks of temporal annotation: anchoring an event to a time expression in the same sentence, anchoring an event to the document creation time, and ordering main events in consecutive sentences. In this paper we describe the TempEval task and the systems that participated in the evaluation. In addition, we describe how further task decomposition can bring even more structure to the evaluation of temporal relations.
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