<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhang-xue-2018-neural">
    <titleInfo>
        <title>Neural Ranking Models for Temporal Dependency Structure Parsing</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yuchen</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Nianwen</namePart>
        <namePart type="family">Xue</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-oct&quot;-&quot;nov</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing</title>
        </titleInfo>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Brussels, Belgium</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We design and build the first neural temporal dependency parser. It utilizes a neural ranking model with minimal feature engineering, and parses time expressions and events in a text into a temporal dependency tree structure. We evaluate our parser on two domains: news reports and narrative stories. In a parsing-only evaluation setup where gold time expressions and events are provided, our parser reaches 0.81 and 0.70 f-score on unlabeled and labeled parsing respectively, a result that is very competitive against alternative approaches. In an end-to-end evaluation setup where time expressions and events are automatically recognized, our parser beats two strong baselines on both data domains. Our experimental results and discussions shed light on the nature of temporal dependency structures in different domains and provide insights that we believe will be valuable to future research in this area.</abstract>
    <identifier type="citekey">zhang-xue-2018-neural</identifier>
    <identifier type="doi">10.18653/v1/D18-1371</identifier>
    <location>
        <url>https://aclanthology.org/D18-1371</url>
    </location>
    <part>
        <date>2018-oct&quot;-&quot;nov</date>
        <extent unit="page">
            <start>3339</start>
            <end>3349</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Neural Ranking Models for Temporal Dependency Structure Parsing
%A Zhang, Yuchen
%A Xue, Nianwen
%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 zhang-xue-2018-neural
%X We design and build the first neural temporal dependency parser. It utilizes a neural ranking model with minimal feature engineering, and parses time expressions and events in a text into a temporal dependency tree structure. We evaluate our parser on two domains: news reports and narrative stories. In a parsing-only evaluation setup where gold time expressions and events are provided, our parser reaches 0.81 and 0.70 f-score on unlabeled and labeled parsing respectively, a result that is very competitive against alternative approaches. In an end-to-end evaluation setup where time expressions and events are automatically recognized, our parser beats two strong baselines on both data domains. Our experimental results and discussions shed light on the nature of temporal dependency structures in different domains and provide insights that we believe will be valuable to future research in this area.
%R 10.18653/v1/D18-1371
%U https://aclanthology.org/D18-1371
%U https://doi.org/10.18653/v1/D18-1371
%P 3339-3349

Markdown (Informal)

[Neural Ranking Models for Temporal Dependency Structure Parsing](https://aclanthology.org/D18-1371) (Zhang & Xue, EMNLP 2018)

• Neural Ranking Models for Temporal Dependency Structure Parsing (Zhang & Xue, EMNLP 2018)

ACL

• Yuchen Zhang and Nianwen Xue. 2018. Neural Ranking Models for Temporal Dependency Structure Parsing. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3339–3349, Brussels, Belgium. Association for Computational Linguistics.