From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations

TACL 2018  ·  Egoitz Laparra, Dongfang Xu, Steven Bethard ·

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 present a new scoring metric for time intervals. We also apply this new metric to carry out a comparative analysis of the annotations of both schemes in the same corpus.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Timex normalization PNT Laparra et al. F1-Score 0.764 # 1


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