<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="vishal-etal-2017-information">
    <titleInfo>
        <title>Information Bottleneck Inspired Method For Chat Text Segmentation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">S</namePart>
        <namePart type="family">Vishal</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Mohit</namePart>
        <namePart type="family">Yadav</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Lovekesh</namePart>
        <namePart type="family">Vig</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Gautam</namePart>
        <namePart type="family">Shroff</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Greg</namePart>
            <namePart type="family">Kondrak</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Taro</namePart>
            <namePart type="family">Watanabe</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Asian Federation of Natural Language Processing</publisher>
            <place>
                <placeTerm type="text">Taipei, Taiwan</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We present a novel technique for segmenting chat conversations using the information bottleneck method (Tishby et al., 2000), augmented with sequential continuity constraints. Furthermore, we utilize critical non-textual clues such as time between two consecutive posts and people mentions within the posts. To ascertain the effectiveness of the proposed method, we have collected data from public Slack conversations and Fresco, a proprietary platform deployed inside our organization. Experiments demonstrate that the proposed method yields an absolute (relative) improvement of as high as 3.23% (11.25%). To facilitate future research, we are releasing manual annotations for segmentation on public Slack conversations.</abstract>
    <identifier type="citekey">vishal-etal-2017-information</identifier>
    <location>
        <url>https://aclanthology.org/I17-1020</url>
    </location>
    <part>
        <date>2017-11</date>
        <extent unit="page">
            <start>194</start>
            <end>203</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Information Bottleneck Inspired Method For Chat Text Segmentation
%A Vishal, S.
%A Yadav, Mohit
%A Vig, Lovekesh
%A Shroff, Gautam
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F vishal-etal-2017-information
%X We present a novel technique for segmenting chat conversations using the information bottleneck method (Tishby et al., 2000), augmented with sequential continuity constraints. Furthermore, we utilize critical non-textual clues such as time between two consecutive posts and people mentions within the posts. To ascertain the effectiveness of the proposed method, we have collected data from public Slack conversations and Fresco, a proprietary platform deployed inside our organization. Experiments demonstrate that the proposed method yields an absolute (relative) improvement of as high as 3.23% (11.25%). To facilitate future research, we are releasing manual annotations for segmentation on public Slack conversations.
%U https://aclanthology.org/I17-1020
%P 194-203

Markdown (Informal)

[Information Bottleneck Inspired Method For Chat Text Segmentation](https://aclanthology.org/I17-1020) (Vishal et al., IJCNLP 2017)

• Information Bottleneck Inspired Method For Chat Text Segmentation (Vishal et al., IJCNLP 2017)

ACL

• S Vishal, Mohit Yadav, Lovekesh Vig, and Gautam Shroff. 2017. Information Bottleneck Inspired Method For Chat Text Segmentation. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 194–203, Taipei, Taiwan. Asian Federation of Natural Language Processing.