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        <title>A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously</title>
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    <name type="personal">
        <namePart type="given">Wenlin</namePart>
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        <namePart type="given">Saipravallika</namePart>
        <namePart type="family">Nettyam</namePart>
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    <name type="personal">
        <namePart type="given">Ruihong</namePart>
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            <title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</title>
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            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <abstract>Capabilities of detecting temporal and causal relations between two events can benefit many applications. Most of existing temporal relation classifiers were trained in a supervised manner. Instead, we explore the observation that regular event pairs show a consistent temporal relation despite of their various contexts and these rich contexts can be used to train a contextual temporal relation classifier, which can further recognize new temporal relation contexts and identify new regular event pairs. We focus on detecting after and before temporal relations and design a weakly supervised learning approach that extracts thousands of regular event pairs and learns a contextual temporal relation classifier simultaneously. Evaluation shows that the acquired regular event pairs are of high quality and contain rich commonsense knowledge and domain specific knowledge. In addition, the weakly supervised trained temporal relation classifier achieves comparable performance with the state-of-the-art supervised systems.</abstract>
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    <identifier type="doi">10.26615/978-954-452-049-6_103</identifier>
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%0 Conference Proceedings
%T A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously
%A Yao, Wenlin
%A Nettyam, Saipravallika
%A Huang, Ruihong
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F yao-etal-2017-weakly
%X Capabilities of detecting temporal and causal relations between two events can benefit many applications. Most of existing temporal relation classifiers were trained in a supervised manner. Instead, we explore the observation that regular event pairs show a consistent temporal relation despite of their various contexts and these rich contexts can be used to train a contextual temporal relation classifier, which can further recognize new temporal relation contexts and identify new regular event pairs. We focus on detecting after and before temporal relations and design a weakly supervised learning approach that extracts thousands of regular event pairs and learns a contextual temporal relation classifier simultaneously. Evaluation shows that the acquired regular event pairs are of high quality and contain rich commonsense knowledge and domain specific knowledge. In addition, the weakly supervised trained temporal relation classifier achieves comparable performance with the state-of-the-art supervised systems.
%R 10.26615/978-954-452-049-6_103
%U https://doi.org/10.26615/978-954-452-049-6_103
%P 803-812

Markdown (Informal)

[A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously](https://doi.org/10.26615/978-954-452-049-6_103) (Yao et al., RANLP 2017)

• A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously (Yao et al., RANLP 2017)

ACL

• Wenlin Yao, Saipravallika Nettyam, and Ruihong Huang. 2017. A Weakly Supervised Approach to Train Temporal Relation Classifiers and Acquire Regular Event Pairs Simultaneously. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 803–812, Varna, Bulgaria. INCOMA Ltd..