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        <title>SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives</title>
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    <name type="personal">
        <namePart type="given">Erik</namePart>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <abstract>An important difference between traditional AI systems and human intelligence is the human ability to harness commonsense knowledge gleaned from a lifetime of learning and experience to make informed decisions. This allows humans to adapt easily to novel situations where AI fails catastrophically due to a lack of situation-specific rules and generalization capabilities. Commonsense knowledge also provides background information that enables humans to successfully operate in social situations where such knowledge is typically assumed. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. Previous versions of SenticNet were focused on collecting this kind of knowledge for sentiment analysis but they were heavily limited by their inability to generalize. SenticNet 4 overcomes such limitations by leveraging on conceptual primitives automatically generated by means of hierarchical clustering and dimensionality reduction.</abstract>
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%0 Conference Proceedings
%T SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives
%A Cambria, Erik
%A Poria, Soujanya
%A Bajpai, Rajiv
%A Schuller, Bjoern
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F cambria-etal-2016-senticnet
%X An important difference between traditional AI systems and human intelligence is the human ability to harness commonsense knowledge gleaned from a lifetime of learning and experience to make informed decisions. This allows humans to adapt easily to novel situations where AI fails catastrophically due to a lack of situation-specific rules and generalization capabilities. Commonsense knowledge also provides background information that enables humans to successfully operate in social situations where such knowledge is typically assumed. Since commonsense consists of information that humans take for granted, gathering it is an extremely difficult task. Previous versions of SenticNet were focused on collecting this kind of knowledge for sentiment analysis but they were heavily limited by their inability to generalize. SenticNet 4 overcomes such limitations by leveraging on conceptual primitives automatically generated by means of hierarchical clustering and dimensionality reduction.
%U https://aclanthology.org/C16-1251
%P 2666-2677

Markdown (Informal)

[SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives](https://aclanthology.org/C16-1251) (Cambria et al., COLING 2016)

• SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives (Cambria et al., COLING 2016)

ACL

• Erik Cambria, Soujanya Poria, Rajiv Bajpai, and Bjoern Schuller. 2016. SenticNet 4: A Semantic Resource for Sentiment Analysis Based on Conceptual Primitives. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2666–2677, Osaka, Japan. The COLING 2016 Organizing Committee.