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        <title>Towards Enhancing Lexical Resource and Using Sense-annotations of OntoSenseNet for Sentiment Analysis</title>
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        <namePart type="given">Sreekavitha</namePart>
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        <namePart type="given">Vijjini</namePart>
        <namePart type="family">Anvesh Rao</namePart>
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    <name type="personal">
        <namePart type="given">Radhika</namePart>
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    <abstract>This paper illustrates the interface of the tool we developed for crowd sourcing and we explain the annotation procedure in detail. Our tool is named as ‘పారుపల్లి పదజాలం’ (Parupalli Padajaalam) which means web of words by Parupalli. The aim of this tool is to populate the OntoSenseNet, sentiment polarity annotated Telugu resource. Recent works have shown the importance of word-level annotations on sentiment analysis. With this as basis, we aim to analyze the importance of sense-annotations obtained from OntoSenseNet in performing the task of sentiment analysis. We explain the features extracted from OntoSenseNet (Telugu). Furthermore we compute and explain the adverbial class distribution of verbs in OntoSenseNet. This task is known to aid in disambiguating word-senses which helps in enhancing the performance of word-sense disambiguation (WSD) task(s).</abstract>
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        <date>2018-08</date>
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%0 Conference Proceedings
%T Towards Enhancing Lexical Resource and Using Sense-annotations of OntoSenseNet for Sentiment Analysis
%A Parupalli, Sreekavitha
%A Anvesh Rao, Vijjini
%A Mamidi, Radhika
%Y Anke, Luis Espinosa
%Y Gromann, Dagmar
%Y Declerck, Thierry
%S Proceedings of the Third Workshop on Semantic Deep Learning
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico
%F parupalli-etal-2018-towards-enhancing
%X This paper illustrates the interface of the tool we developed for crowd sourcing and we explain the annotation procedure in detail. Our tool is named as ‘పారుపల్లి పదజాలం’ (Parupalli Padajaalam) which means web of words by Parupalli. The aim of this tool is to populate the OntoSenseNet, sentiment polarity annotated Telugu resource. Recent works have shown the importance of word-level annotations on sentiment analysis. With this as basis, we aim to analyze the importance of sense-annotations obtained from OntoSenseNet in performing the task of sentiment analysis. We explain the features extracted from OntoSenseNet (Telugu). Furthermore we compute and explain the adverbial class distribution of verbs in OntoSenseNet. This task is known to aid in disambiguating word-senses which helps in enhancing the performance of word-sense disambiguation (WSD) task(s).
%U https://aclanthology.org/W18-4005
%P 39-44

Markdown (Informal)

[Towards Enhancing Lexical Resource and Using Sense-annotations of OntoSenseNet for Sentiment Analysis](https://aclanthology.org/W18-4005) (Parupalli et al., SemDeep 2018)

• Towards Enhancing Lexical Resource and Using Sense-annotations of OntoSenseNet for Sentiment Analysis (Parupalli et al., SemDeep 2018)

ACL

• Sreekavitha Parupalli, Vijjini Anvesh Rao, and Radhika Mamidi. 2018. Towards Enhancing Lexical Resource and Using Sense-annotations of OntoSenseNet for Sentiment Analysis. In Proceedings of the Third Workshop on Semantic Deep Learning, pages 39–44, Santa Fe, New Mexico. Association for Computational Linguistics.