<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="pisarevskaya-etal-2017-deception">
    <titleInfo>
        <title>Deception Detection for the Russian Language: Lexical and Syntactic Parameters</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Dina</namePart>
        <namePart type="family">Pisarevskaya</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Tatiana</namePart>
        <namePart type="family">Litvinova</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Olga</namePart>
        <namePart type="family">Litvinova</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 1st Workshop on Natural Language Processing and Information Retrieval associated with RANLP 2017</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Mireille</namePart>
            <namePart type="family">Makary</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Michael</namePart>
            <namePart type="family">Oakes</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>INCOMA Inc.</publisher>
            <place>
                <placeTerm type="text">Varna, Bulgaria</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The field of automated deception detection in written texts is methodologically challenging. Different linguistic levels (lexics, syntax and semantics) are basically used for different types of English texts to reveal if they are truthful or deceptive. Such parameters as POS tags and POS tags n-grams, punctuation marks, sentiment polarity of words, psycholinguistic features, fragments of syntaсtic structures are taken into consideration. The importance of different types of parameters was not compared for the Russian language before and should be investigated before moving to complex models and higher levels of linguistic processing. On the example of the Russian Deception Bank Corpus we estimate the impact of three groups of features (POS features including bigrams, sentiment and psycholinguistic features, syntax and readability features) on the successful deception detection and find out that POS features can be used for binary text classification, but the results should be double-checked and, if possible, improved.</abstract>
    <identifier type="citekey">pisarevskaya-etal-2017-deception</identifier>
    <identifier type="doi">10.26615/978-954-452-038-0_001</identifier>
    <part>
        <date>2017-09</date>
        <extent unit="page">
            <start>1</start>
            <end>10</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Deception Detection for the Russian Language: Lexical and Syntactic Parameters
%A Pisarevskaya, Dina
%A Litvinova, Tatiana
%A Litvinova, Olga
%Y Makary, Mireille
%Y Oakes, Michael
%S Proceedings of the 1st Workshop on Natural Language Processing and Information Retrieval associated with RANLP 2017
%D 2017
%8 September
%I INCOMA Inc.
%C Varna, Bulgaria
%F pisarevskaya-etal-2017-deception
%X The field of automated deception detection in written texts is methodologically challenging. Different linguistic levels (lexics, syntax and semantics) are basically used for different types of English texts to reveal if they are truthful or deceptive. Such parameters as POS tags and POS tags n-grams, punctuation marks, sentiment polarity of words, psycholinguistic features, fragments of syntaсtic structures are taken into consideration. The importance of different types of parameters was not compared for the Russian language before and should be investigated before moving to complex models and higher levels of linguistic processing. On the example of the Russian Deception Bank Corpus we estimate the impact of three groups of features (POS features including bigrams, sentiment and psycholinguistic features, syntax and readability features) on the successful deception detection and find out that POS features can be used for binary text classification, but the results should be double-checked and, if possible, improved.
%R 10.26615/978-954-452-038-0_001
%U https://doi.org/10.26615/978-954-452-038-0_001
%P 1-10

Markdown (Informal)

[Deception Detection for the Russian Language: Lexical and Syntactic Parameters](https://doi.org/10.26615/978-954-452-038-0_001) (Pisarevskaya et al., RANLP 2017)

• Deception Detection for the Russian Language: Lexical and Syntactic Parameters (Pisarevskaya et al., RANLP 2017)

ACL

• Dina Pisarevskaya, Tatiana Litvinova, and Olga Litvinova. 2017. Deception Detection for the Russian Language: Lexical and Syntactic Parameters. In Proceedings of the 1st Workshop on Natural Language Processing and Information Retrieval associated with RANLP 2017, pages 1–10, Varna, Bulgaria. INCOMA Inc..