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        <title>From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations</title>
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    <abstract>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.</abstract>
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%0 Journal Article
%T From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations
%A Laparra, Egoitz
%A Xu, Dongfang
%A Bethard, Steven
%J Transactions of the Association for Computational Linguistics
%D 2018
%V 6
%I MIT Press
%C Cambridge, MA
%F laparra-etal-2018-characters
%X 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.
%R 10.1162/tacl_a_00025
%U https://aclanthology.org/Q18-1025
%U https://doi.org/10.1162/tacl_a_00025
%P 343-356

Markdown (Informal)

[From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations](https://aclanthology.org/Q18-1025) (Laparra et al., TACL 2018)

• From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations (Laparra et al., TACL 2018)

ACL

• Egoitz Laparra, Dongfang Xu, and Steven Bethard. 2018. From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations. Transactions of the Association for Computational Linguistics, 6:343–356.