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        <title>Temporal Information Extraction by Predicting Relative Time-lines</title>
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            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Ellen</namePart>
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    <abstract>The current leading paradigm for temporal information extraction from text consists of three phases: (1) recognition of events and temporal expressions, (2) recognition of temporal relations among them, and (3) time-line construction from the temporal relations. In contrast to the first two phases, the last phase, time-line construction, received little attention and is the focus of this work. In this paper, we propose a new method to construct a linear time-line from a set of (extracted) temporal relations. But more importantly, we propose a novel paradigm in which we directly predict start and end-points for events from the text, constituting a time-line without going through the intermediate step of prediction of temporal relations as in earlier work. Within this paradigm, we propose two models that predict in linear complexity, and a new training loss using TimeML-style annotations, yielding promising results.</abstract>
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    <identifier type="doi">10.18653/v1/D18-1155</identifier>
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%0 Conference Proceedings
%T Temporal Information Extraction by Predicting Relative Time-lines
%A Leeuwenberg, Artuur
%A Moens, Marie-Francine
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F leeuwenberg-moens-2018-temporal
%X The current leading paradigm for temporal information extraction from text consists of three phases: (1) recognition of events and temporal expressions, (2) recognition of temporal relations among them, and (3) time-line construction from the temporal relations. In contrast to the first two phases, the last phase, time-line construction, received little attention and is the focus of this work. In this paper, we propose a new method to construct a linear time-line from a set of (extracted) temporal relations. But more importantly, we propose a novel paradigm in which we directly predict start and end-points for events from the text, constituting a time-line without going through the intermediate step of prediction of temporal relations as in earlier work. Within this paradigm, we propose two models that predict in linear complexity, and a new training loss using TimeML-style annotations, yielding promising results.
%R 10.18653/v1/D18-1155
%U https://aclanthology.org/D18-1155
%U https://doi.org/10.18653/v1/D18-1155
%P 1237-1246

Markdown (Informal)

[Temporal Information Extraction by Predicting Relative Time-lines](https://aclanthology.org/D18-1155) (Leeuwenberg & Moens, EMNLP 2018)

• Temporal Information Extraction by Predicting Relative Time-lines (Leeuwenberg & Moens, EMNLP 2018)

ACL

• Artuur Leeuwenberg and Marie-Francine Moens. 2018. Temporal Information Extraction by Predicting Relative Time-lines. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 1237–1246, Brussels, Belgium. Association for Computational Linguistics.