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    <titleInfo>
        <title>A Graph Based Semi-Supervised Approach for Analysis of Derivational Nouns in Sanskrit</title>
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    <name type="personal">
        <namePart type="given">Amrith</namePart>
        <namePart type="family">Krishna</namePart>
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    <name type="personal">
        <namePart type="given">Pavankumar</namePart>
        <namePart type="family">Satuluri</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Harshavardhan</namePart>
        <namePart type="family">Ponnada</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Muneeb</namePart>
        <namePart type="family">Ahmed</namePart>
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            <roleTerm authority="marcrelator" type="text">author</roleTerm>
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    <name type="personal">
        <namePart type="given">Gulab</namePart>
        <namePart type="family">Arora</namePart>
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    <name type="personal">
        <namePart type="given">Kaustubh</namePart>
        <namePart type="family">Hiware</namePart>
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    <name type="personal">
        <namePart type="given">Pawan</namePart>
        <namePart type="family">Goyal</namePart>
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        <dateIssued>2017-aug</dateIssued>
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            <title>Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing</title>
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            <publisher>Association for Computational Linguistics</publisher>
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    <abstract>Derivational nouns are widely used in Sanskrit corpora and represent an important cornerstone of productivity in the language. Currently there exists no analyser that identifies the derivational nouns. We propose a semi supervised approach for identification of derivational nouns in Sanskrit. We not only identify the derivational words, but also link them to their corresponding source words. Our novelty comes in the design of the network structure for the task. The edge weights are featurised based on the phonetic, morphological, syntactic and the semantic similarity shared between the words to be identified. We find that our model is effective for the task, even when we employ a labelled dataset which is only 5 % to that of the entire dataset.</abstract>
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    <identifier type="doi">10.18653/v1/W17-2409</identifier>
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        <date>2017-aug</date>
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            <start>66</start>
            <end>75</end>
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%0 Conference Proceedings
%T A Graph Based Semi-Supervised Approach for Analysis of Derivational Nouns in Sanskrit
%A Krishna, Amrith
%A Satuluri, Pavankumar
%A Ponnada, Harshavardhan
%A Ahmed, Muneeb
%A Arora, Gulab
%A Hiware, Kaustubh
%A Goyal, Pawan
%S Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing
%D 2017
%8 aug
%I Association for Computational Linguistics
%C Vancouver, Canada
%F krishna-etal-2017-graph
%X Derivational nouns are widely used in Sanskrit corpora and represent an important cornerstone of productivity in the language. Currently there exists no analyser that identifies the derivational nouns. We propose a semi supervised approach for identification of derivational nouns in Sanskrit. We not only identify the derivational words, but also link them to their corresponding source words. Our novelty comes in the design of the network structure for the task. The edge weights are featurised based on the phonetic, morphological, syntactic and the semantic similarity shared between the words to be identified. We find that our model is effective for the task, even when we employ a labelled dataset which is only 5 % to that of the entire dataset.
%R 10.18653/v1/W17-2409
%U https://aclanthology.org/W17-2409
%U https://doi.org/10.18653/v1/W17-2409
%P 66-75

Markdown (Informal)

[A Graph Based Semi-Supervised Approach for Analysis of Derivational Nouns in Sanskrit](https://aclanthology.org/W17-2409) (Krishna et al., 2017)

• A Graph Based Semi-Supervised Approach for Analysis of Derivational Nouns in Sanskrit (Krishna et al., 2017)

ACL

• Amrith Krishna, Pavankumar Satuluri, Harshavardhan Ponnada, Muneeb Ahmed, Gulab Arora, Kaustubh Hiware, and Pawan Goyal. 2017. A Graph Based Semi-Supervised Approach for Analysis of Derivational Nouns in Sanskrit. In Proceedings of TextGraphs-11: the Workshop on Graph-based Methods for Natural Language Processing, pages 66–75, Vancouver, Canada. Association for Computational Linguistics.