<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="stajner-hulpus-2018-automatic">
    <titleInfo>
        <title>Automatic Assessment of Conceptual Text Complexity Using Knowledge Graphs</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Sanja</namePart>
        <namePart type="family">Štajner</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ioana</namePart>
        <namePart type="family">Hulpuş</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Emily</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Bender</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Leon</namePart>
            <namePart type="family">Derczynski</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Pierre</namePart>
            <namePart type="family">Isabelle</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Santa Fe, New Mexico, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Complexity of texts is usually assessed only at the lexical and syntactic levels. Although it is known that conceptual complexity plays a significant role in text understanding, no attempts have been made at assessing it automatically. We propose to automatically estimate the conceptual complexity of texts by exploiting a number of graph-based measures on a large knowledge base. By using a high-quality language learners corpus for English, we show that graph-based measures of individual text concepts, as well as the way they relate to each other in the knowledge graph, have a high discriminative power when distinguishing between two versions of the same text. Furthermore, when used as features in a binary classification task aiming to choose the simpler of two versions of the same text, our measures achieve high performance even in a default setup.</abstract>
    <identifier type="citekey">stajner-hulpus-2018-automatic</identifier>
    <location>
        <url>https://aclanthology.org/C18-1027/</url>
    </location>
    <part>
        <date>2018-08</date>
        <extent unit="page">
            <start>318</start>
            <end>330</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Automatic Assessment of Conceptual Text Complexity Using Knowledge Graphs
%A Štajner, Sanja
%A Hulpuş, Ioana
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F stajner-hulpus-2018-automatic
%X Complexity of texts is usually assessed only at the lexical and syntactic levels. Although it is known that conceptual complexity plays a significant role in text understanding, no attempts have been made at assessing it automatically. We propose to automatically estimate the conceptual complexity of texts by exploiting a number of graph-based measures on a large knowledge base. By using a high-quality language learners corpus for English, we show that graph-based measures of individual text concepts, as well as the way they relate to each other in the knowledge graph, have a high discriminative power when distinguishing between two versions of the same text. Furthermore, when used as features in a binary classification task aiming to choose the simpler of two versions of the same text, our measures achieve high performance even in a default setup.
%U https://aclanthology.org/C18-1027/
%P 318-330

Markdown (Informal)

[Automatic Assessment of Conceptual Text Complexity Using Knowledge Graphs](https://aclanthology.org/C18-1027/) (Štajner & Hulpuş, COLING 2018)

• Automatic Assessment of Conceptual Text Complexity Using Knowledge Graphs (Štajner & Hulpuş, COLING 2018)

ACL

• Sanja Štajner and Ioana Hulpuş. 2018. Automatic Assessment of Conceptual Text Complexity Using Knowledge Graphs. In Proceedings of the 27th International Conference on Computational Linguistics, pages 318–330, Santa Fe, New Mexico, USA. Association for Computational Linguistics.