<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="tsekouras-etal-2017-graph">
    <titleInfo>
        <title>A Graph-based Text Similarity Measure That Employs Named Entity Information</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Leonidas</namePart>
        <namePart type="family">Tsekouras</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Iraklis</namePart>
        <namePart type="family">Varlamis</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">George</namePart>
        <namePart type="family">Giannakopoulos</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2017-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>INCOMA Ltd.</publisher>
            <place>
                <placeTerm type="text">Varna, Bulgaria</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Text comparison is an interesting though hard task, with many applications in Natural Language Processing. This work introduces a new text-similarity measure, which employs named-entities’ information extracted from the texts and the n-gram graphs’ model for representing documents. Using OpenCalais as a named-entity recognition service and the JINSECT toolkit for constructing and managing n-gram graphs, the text similarity measure is embedded in a text clustering algorithm (k-Means). The evaluation of the produced clusters with various clustering validity metrics shows that the extraction of named entities at a first step can be profitable for the time-performance of similarity measures that are based on the n-gram graph representation without affecting the overall performance of the NLP task.</abstract>
    <identifier type="citekey">tsekouras-etal-2017-graph</identifier>
    <identifier type="doi">10.26615/978-954-452-049-6_098</identifier>
    <part>
        <date>2017-09</date>
        <extent unit="page">
            <start>765</start>
            <end>771</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A Graph-based Text Similarity Measure That Employs Named Entity Information
%A Tsekouras, Leonidas
%A Varlamis, Iraklis
%A Giannakopoulos, George
%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 tsekouras-etal-2017-graph
%X Text comparison is an interesting though hard task, with many applications in Natural Language Processing. This work introduces a new text-similarity measure, which employs named-entities’ information extracted from the texts and the n-gram graphs’ model for representing documents. Using OpenCalais as a named-entity recognition service and the JINSECT toolkit for constructing and managing n-gram graphs, the text similarity measure is embedded in a text clustering algorithm (k-Means). The evaluation of the produced clusters with various clustering validity metrics shows that the extraction of named entities at a first step can be profitable for the time-performance of similarity measures that are based on the n-gram graph representation without affecting the overall performance of the NLP task.
%R 10.26615/978-954-452-049-6_098
%U https://doi.org/10.26615/978-954-452-049-6_098
%P 765-771

Markdown (Informal)

[A Graph-based Text Similarity Measure That Employs Named Entity Information](https://doi.org/10.26615/978-954-452-049-6_098) (Tsekouras et al., RANLP 2017)

• A Graph-based Text Similarity Measure That Employs Named Entity Information (Tsekouras et al., RANLP 2017)

ACL

• Leonidas Tsekouras, Iraklis Varlamis, and George Giannakopoulos. 2017. A Graph-based Text Similarity Measure That Employs Named Entity Information. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 765–771, Varna, Bulgaria. INCOMA Ltd..