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        <title>Event Time Extraction with a Decision Tree of Neural Classifiers</title>
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        <namePart type="given">Nils</namePart>
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    <name type="personal">
        <namePart type="given">Nazanin</namePart>
        <namePart type="family">Dehghani</namePart>
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    <name type="personal">
        <namePart type="given">Iryna</namePart>
        <namePart type="family">Gurevych</namePart>
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    <abstract>Extracting the information from text when an event happened is challenging. Documents do not only report on current events, but also on past events as well as on future events. Often, the relevant time information for an event is scattered across the document. In this paper we present a novel method to automatically anchor events in time. To our knowledge it is the first approach that takes temporal information from the complete document into account. We created a decision tree that applies neural network based classifiers at its nodes. We use this tree to incrementally infer, in a stepwise manner, at which time frame an event happened. We evaluate the approach on the TimeBank-EventTime Corpus (Reimers et al., 2016) achieving an accuracy of 42.0% compared to an inter-annotator agreement (IAA) of 56.7%. For events that span over a single day we observe an accuracy improvement of 33.1 points compared to the state-of-the-art CAEVO system (Chambers et al., 2014). Without retraining, we apply this model to the SemEval-2015 Task 4 on automatic timeline generation and achieve an improvement of 4.01 points F1-score compared to the state-of-the-art. Our code is publically available.</abstract>
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    <identifier type="doi">10.1162/tacl_a_00006</identifier>
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        <date>2018</date>
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%0 Journal Article
%T Event Time Extraction with a Decision Tree of Neural Classifiers
%A Reimers, Nils
%A Dehghani, Nazanin
%A Gurevych, Iryna
%J Transactions of the Association for Computational Linguistics
%D 2018
%V 6
%I MIT Press
%C Cambridge, MA
%F reimers-etal-2018-event
%X Extracting the information from text when an event happened is challenging. Documents do not only report on current events, but also on past events as well as on future events. Often, the relevant time information for an event is scattered across the document. In this paper we present a novel method to automatically anchor events in time. To our knowledge it is the first approach that takes temporal information from the complete document into account. We created a decision tree that applies neural network based classifiers at its nodes. We use this tree to incrementally infer, in a stepwise manner, at which time frame an event happened. We evaluate the approach on the TimeBank-EventTime Corpus (Reimers et al., 2016) achieving an accuracy of 42.0% compared to an inter-annotator agreement (IAA) of 56.7%. For events that span over a single day we observe an accuracy improvement of 33.1 points compared to the state-of-the-art CAEVO system (Chambers et al., 2014). Without retraining, we apply this model to the SemEval-2015 Task 4 on automatic timeline generation and achieve an improvement of 4.01 points F1-score compared to the state-of-the-art. Our code is publically available.
%R 10.1162/tacl_a_00006
%U https://aclanthology.org/Q18-1006
%U https://doi.org/10.1162/tacl_a_00006
%P 77-89

Markdown (Informal)

[Event Time Extraction with a Decision Tree of Neural Classifiers](https://aclanthology.org/Q18-1006) (Reimers et al., TACL 2018)

• Event Time Extraction with a Decision Tree of Neural Classifiers (Reimers et al., TACL 2018)

ACL

• Nils Reimers, Nazanin Dehghani, and Iryna Gurevych. 2018. Event Time Extraction with a Decision Tree of Neural Classifiers. Transactions of the Association for Computational Linguistics, 6:77–89.