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From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations
2018
Transactions of the Association for Computational Linguistics
This paper presents the first model for time normalization trained on the SCATE corpus. In the SCATE schema, time expressions are annotated as a semantic composition of time entities. This novel schema favors machine learning approaches, as it can be viewed as a semantic parsing task. In this work, we propose a character level multi-output neural network that outperforms previous state-of-the-art built on the TimeML schema. To compare predictions of systems that follow both SCATE and TimeML, we
doi:10.1162/tacl_a_00025
pmid:32432133
pmcid:PMC7236559
fatcat:zngqwhh5xvbu3dsnymzmdup7ee