<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="hida-etal-2018-dynamic">
    <titleInfo>
        <title>Dynamic and Static Topic Model for Analyzing Time-Series Document Collections</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Rem</namePart>
        <namePart type="family">Hida</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Naoya</namePart>
        <namePart type="family">Takeishi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Takehisa</namePart>
        <namePart type="family">Yairi</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Koichi</namePart>
        <namePart type="family">Hori</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yusuke</namePart>
            <namePart type="family">Miyao</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Melbourne, Australia</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>For extracting meaningful topics from texts, their structures should be considered properly. In this paper, we aim to analyze structured time-series documents such as a collection of news articles and a series of scientific papers, wherein topics evolve along time depending on multiple topics in the past and are also related to each other at each time. To this end, we propose a dynamic and static topic model, which simultaneously considers the dynamic structures of the temporal topic evolution and the static structures of the topic hierarchy at each time. We show the results of experiments on collections of scientific papers, in which the proposed method outperformed conventional models. Moreover, we show an example of extracted topic structures, which we found helpful for analyzing research activities.</abstract>
    <identifier type="citekey">hida-etal-2018-dynamic</identifier>
    <identifier type="doi">10.18653/v1/P18-2082</identifier>
    <location>
        <url>https://aclanthology.org/P18-2082</url>
    </location>
    <part>
        <date>2018-07</date>
        <extent unit="page">
            <start>516</start>
            <end>520</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Dynamic and Static Topic Model for Analyzing Time-Series Document Collections
%A Hida, Rem
%A Takeishi, Naoya
%A Yairi, Takehisa
%A Hori, Koichi
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F hida-etal-2018-dynamic
%X For extracting meaningful topics from texts, their structures should be considered properly. In this paper, we aim to analyze structured time-series documents such as a collection of news articles and a series of scientific papers, wherein topics evolve along time depending on multiple topics in the past and are also related to each other at each time. To this end, we propose a dynamic and static topic model, which simultaneously considers the dynamic structures of the temporal topic evolution and the static structures of the topic hierarchy at each time. We show the results of experiments on collections of scientific papers, in which the proposed method outperformed conventional models. Moreover, we show an example of extracted topic structures, which we found helpful for analyzing research activities.
%R 10.18653/v1/P18-2082
%U https://aclanthology.org/P18-2082
%U https://doi.org/10.18653/v1/P18-2082
%P 516-520

Markdown (Informal)

[Dynamic and Static Topic Model for Analyzing Time-Series Document Collections](https://aclanthology.org/P18-2082) (Hida et al., ACL 2018)

• Dynamic and Static Topic Model for Analyzing Time-Series Document Collections (Hida et al., ACL 2018)

ACL

• Rem Hida, Naoya Takeishi, Takehisa Yairi, and Koichi Hori. 2018. Dynamic and Static Topic Model for Analyzing Time-Series Document Collections. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 516–520, Melbourne, Australia. Association for Computational Linguistics.