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    <titleInfo>
        <title>Identification of Character Adjectives from Mahabharata</title>
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    <name type="personal">
        <namePart type="given">Apurba</namePart>
        <namePart type="family">Paul</namePart>
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    <name type="personal">
        <namePart type="given">Dipankar</namePart>
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        <dateIssued>2017-09</dateIssued>
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            <title>Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017</title>
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        <name type="personal">
            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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    <abstract>The present paper describes the identification of prominent characters and their adjectives from Indian mythological epic, Mahabharata, written in English texts. However, in contrast to the tra-ditional approaches of named entity identifica-tion, the present system extracts hidden attributes associated with each of the characters (e.g., character adjectives). We observed distinct phrase level linguistic patterns that hint the pres-ence of characters in different text spans. Such six patterns were used in order to extract the cha-racters. On the other hand, a distinguishing set of novel features (e.g., multi-word expression, nodes and paths of parse tree, immediate ancestors etc.) was employed. Further, the correlation of the features is also measured in order to identify the important features. Finally, we applied various machine learning algorithms (e.g., Naive Bayes, KNN, Logistic Regression, Decision Tree, Random Forest etc.) along with deep learning to classify the patterns as characters or non-characters in order to achieve decent accuracy. Evaluation shows that phrase level linguistic patterns as well as the adopted features are highly active in capturing characters and their adjectives.</abstract>
    <identifier type="citekey">paul-das-2017-identification</identifier>
    <identifier type="doi">10.26615/978-954-452-049-6_074</identifier>
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        <date>2017-09</date>
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            <start>569</start>
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%0 Conference Proceedings
%T Identification of Character Adjectives from Mahabharata
%A Paul, Apurba
%A Das, Dipankar
%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 paul-das-2017-identification
%X The present paper describes the identification of prominent characters and their adjectives from Indian mythological epic, Mahabharata, written in English texts. However, in contrast to the tra-ditional approaches of named entity identifica-tion, the present system extracts hidden attributes associated with each of the characters (e.g., character adjectives). We observed distinct phrase level linguistic patterns that hint the pres-ence of characters in different text spans. Such six patterns were used in order to extract the cha-racters. On the other hand, a distinguishing set of novel features (e.g., multi-word expression, nodes and paths of parse tree, immediate ancestors etc.) was employed. Further, the correlation of the features is also measured in order to identify the important features. Finally, we applied various machine learning algorithms (e.g., Naive Bayes, KNN, Logistic Regression, Decision Tree, Random Forest etc.) along with deep learning to classify the patterns as characters or non-characters in order to achieve decent accuracy. Evaluation shows that phrase level linguistic patterns as well as the adopted features are highly active in capturing characters and their adjectives.
%R 10.26615/978-954-452-049-6_074
%U https://doi.org/10.26615/978-954-452-049-6_074
%P 569-576

Markdown (Informal)

[Identification of Character Adjectives from Mahabharata](https://doi.org/10.26615/978-954-452-049-6_074) (Paul & Das, RANLP 2017)

• Identification of Character Adjectives from Mahabharata (Paul & Das, RANLP 2017)

ACL

• Apurba Paul and Dipankar Das. 2017. Identification of Character Adjectives from Mahabharata. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 569–576, Varna, Bulgaria. INCOMA Ltd..