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        <title>What makes us laugh? Investigations into Automatic Humor Classification</title>
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    <name type="personal">
        <namePart type="given">Vikram</namePart>
        <namePart type="family">Ahuja</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Navjyoti</namePart>
        <namePart type="family">Singh</namePart>
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            <title>Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media</title>
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        <name type="personal">
            <namePart type="given">Malvina</namePart>
            <namePart type="family">Nissim</namePart>
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        <name type="personal">
            <namePart type="given">Viviana</namePart>
            <namePart type="family">Patti</namePart>
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    <abstract>Most scholarly works in the field of computational detection of humour derive their inspiration from the incongruity theory. Incongruity is an indispensable facet in drawing a line between humorous and non-humorous occurrences but is immensely inadequate in shedding light on what actually made the particular occurrence a funny one. Classical theories like Script-based Semantic Theory of Humour and General Verbal Theory of Humour try and achieve this feat to an adequate extent. In this paper we adhere to a more holistic approach towards classification of humour based on these classical theories with a few improvements and revisions. Through experiments based on our linear approach and performed on large data-sets of jokes, we are able to demonstrate the adaptability and show componentizability of our model, and that a host of classification techniques can be used to overcome the challenging problem of distinguishing between various categories and sub-categories of jokes.</abstract>
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        <date>2018-06</date>
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%0 Conference Proceedings
%T What makes us laugh? Investigations into Automatic Humor Classification
%A Ahuja, Vikram
%A Bali, Taradheesh
%A Singh, Navjyoti
%Y Nissim, Malvina
%Y Patti, Viviana
%Y Plank, Barbara
%Y Wagner, Claudia
%S Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana, USA
%F ahuja-etal-2018-makes
%X Most scholarly works in the field of computational detection of humour derive their inspiration from the incongruity theory. Incongruity is an indispensable facet in drawing a line between humorous and non-humorous occurrences but is immensely inadequate in shedding light on what actually made the particular occurrence a funny one. Classical theories like Script-based Semantic Theory of Humour and General Verbal Theory of Humour try and achieve this feat to an adequate extent. In this paper we adhere to a more holistic approach towards classification of humour based on these classical theories with a few improvements and revisions. Through experiments based on our linear approach and performed on large data-sets of jokes, we are able to demonstrate the adaptability and show componentizability of our model, and that a host of classification techniques can be used to overcome the challenging problem of distinguishing between various categories and sub-categories of jokes.
%R 10.18653/v1/W18-1101
%U https://aclanthology.org/W18-1101
%U https://doi.org/10.18653/v1/W18-1101
%P 1-9

Markdown (Informal)

[What makes us laugh? Investigations into Automatic Humor Classification](https://aclanthology.org/W18-1101) (Ahuja et al., PEOPLES 2018)

• What makes us laugh? Investigations into Automatic Humor Classification (Ahuja et al., PEOPLES 2018)

ACL

• Vikram Ahuja, Taradheesh Bali, and Navjyoti Singh. 2018. What makes us laugh? Investigations into Automatic Humor Classification. In Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media, pages 1–9, New Orleans, Louisiana, USA. Association for Computational Linguistics.