Laughter Abounds in the Mouths of Computers: Investigations in Automatic Humor Recognition

Mihalcea, Rada; Strapparava, Carlo

doi:10.1007/11590323_9

Cited by 65 publications

(126 citation statements)

References 4 publications

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“…In a very similar study to ours [6] an analysis of the jokes in their corpus showed some possibly useful characteristics of jokes. For instance, they often contain human related words such as "you" or "I"; they often use negations, dirty words, antonymy etc.…”

Section: Stylesupporting

confidence: 72%

“…In our experiments we instead use quite low level information sources and see if enough hints can be gathered to determine if something is a joke or not without actually having any understanding of the meaning. Our work closely resembles the work in [6], where humor detection was treated as a text classification problem. Machine learning using content based information and some stylistic features present in many jokes gave results far above baseline performance.…”

Section: Introductionmentioning

confidence: 86%

“…word play jokes, have been constructed [1][2][3][4]. The second is humor recognition, where systems to recognize whether a text is a joke or not have been constructed [5,6], which is what this paper is about.…”

Section: Introductionmentioning

confidence: 99%

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Recognizing Humor Without Recognizing Meaning

Sjöbergh

Araki

Applications of Fuzzy Sets Theory

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Abstract. We present a machine learning approach for classifying sentences as one-liner jokes or normal sentences. We use no deep analysis of the meaning to try to see if it is humorous, instead we rely on a combination of simple features to see if these are enough to detect humor. Features such as word overlap with other jokes, presence of words common in jokes, ambiguity and word overlap with common idioms turn out to be useful. When training and testing on equal amounts of jokes and sentences from the British National Corpus, a classification accuracy of 85% is achieved.

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Section: Stylesupporting

confidence: 72%

Section: Introductionmentioning

confidence: 86%

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Recognizing Humor Without Recognizing Meaning

Sjöbergh

Araki

Applications of Fuzzy Sets Theory

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“…First, 150 one-liners were randomly selected from the humorous data set used in [13]. A one-liner is a short sentence with comic effects and an interesting linguistic structure: simple syntax, deliberate use of rhetoric devices (e.g.…”

Section: Datamentioning

confidence: 99%

“…One model (LSA on BNC) is trained on the British National Corpus (BNC) -a balanced corpus covering different styles, genres and domains. A second model (LSA on jokes) is trained on a corpus of 16,000 one-liner jokes, which was automatically mined from the Web [13].…”

Section: Corpus-based Semantic Relatednessmentioning

confidence: 99%

Computational Models for Incongruity Detection in Humour

Mihalcea¹,

Strapparava²,

Pulman³

2010

Lecture Notes in Computer Science

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Abstract. Incongruity resolution is one of the most widely accepted theories of humour, suggesting that humour is due to the mixing of two disparate interpretation frames in one statement. In this paper, we explore several computational models for incongruity resolution. We introduce a new data set, consisting of a series of 'set-ups' (preparations for a punch line), each of them followed by four possible coherent continuations out of which only one has a comic effect. Using this data set, we redefine the task as the automatic identification of the humorous punch line among all the plausible endings. We explore several measures of semantic relatedness, along with a number of joke-specific features, and try to understand their appropriateness as computational models for incongruity detection.

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CLEF Workshop JOKER: Automatic Wordplay and Humour Translation

Ermakova

Miller

Puchalski

et al. 2022

Lecture Notes in Computer Science

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Humour remains one of the most difficult aspects of intercultural communication: understanding humour often requires understanding implicit cultural references and/or double meanings, and this raises the question of its (un)translatability. Wordplay is a common source of humour in due to its attention-getting and subversive character. The translation of humour and wordplay is therefore in high demand. Modern translation depends heavily on technological aids, yet few works have treated the automation of humour and wordplay translation, or the creation of humour corpora. The goal of the JOKER workshop is to bring together translators and computer scientists to work on an evaluation framework for wordplay, including data and metric development, and to foster work on automatic methods for wordplay translation. We propose three pilot tasks: (1) classify and explain instances of wordplay, (2) translate single words containing wordplay, and (3) translate entire phrases containing wordplay.

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Laughter Abounds in the Mouths of Computers: Investigations in Automatic Humor Recognition

Cited by 65 publications

References 4 publications

Recognizing Humor Without Recognizing Meaning

Recognizing Humor Without Recognizing Meaning

Computational Models for Incongruity Detection in Humour

CLEF Workshop JOKER: Automatic Wordplay and Humour Translation

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