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Within the SemEval-2013 evaluation exercise, the TempEval-3 shared task aims to advance research on temporal information processing. It follows on from TempEval-1 and-2, with: a three-part structure covering temporal expression, event, and temporal relation extraction; a larger dataset; and new single measures to rank systems – in each task and in general.(More)
Extracting temporal information from raw text is fundamental for deep language understanding , and key to many applications like question answering, information extraction, and document summarization. In this paper, we describe two systems we submitted to the TempEval 2 challenge, for extracting temporal information from raw text. The systems use a(More)
In this proposal, we describe the forthcoming TempEval-3 task which is currently in preparation for the SemEval-2013 evaluation exercise. The aim of TempEval is to advance research on temporal information processing. TempEval-3 follows on from previous TempEval events, incorporating: a three-part task structure covering event, temporal expression and(More)
In this paper we propose a new method for evaluating systems that extract temporal information from text. It uses temporal closure 1 to reward relations that are equivalent but distinct. Our metric measures the overall performance of systems with a single score, making comparison between different systems straightforward. Our approach is easy to implement,(More)
QA TempEval shifts the goal of previous TempEvals away from an intrinsic evaluation methodology toward a more extrinsic goal of question answering. This evaluation requires systems to capture temporal information relevant to perform an end-user task, as opposed to corpus-based evaluation where all temporal information is equally important. Evaluation(More)
—The temporal annotation scheme TimeML was developed to support research in complex temporal question answering (QA). Given the complexity of temporal QA, most of the efforts have focused, so far, on extracting temporal information, which has been evaluated with corpus-based evaluation. However, the QA task represents a natural way to evaluate temporal(More)
Extracting temporal information from raw text is fundamental for deep language understanding, and key to many applications like question answering, information extraction, and document summarization. Our long-term goal is to build complete temporal structure of documents and use the temporal structure in other applications like textual entailment, question(More)