Computational Humor Research Papers - Academia.edu (original) (raw)

Humor is one of the most interesting and puzzling aspects of human behavior. Despite the attention it has received in fields such as philosophy, linguistics, and psychology, there have been only few attempts to create computational models for humor recognition or generation. In this paper, we bring empirical evidence that computational approaches can be successfully applied to the task of humor recognition. Through experiments performed on very large data sets, we show that automatic classification techniques can be effectively used to distinguish between humorous and non-humorous texts, with significant improvements observed over apriori known baselines.

- by
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- Computational Humor, Computer Model, Human behavior, Empirical evidence

Satire is an attractive subject in deception detection research: it is a type of deception that intentionally incorporates cues revealing its own deceptiveness. Whereas other types of fabrications aim to instill a false sense of truth in the reader, a successful satirical hoax must eventually be exposed as a jest. This paper provides a conceptual overview of satire and humor, elaborating and illustrating the unique features of satirical news, which mimics the format and style of journalistic reporting. Satirical news stories were carefully matched and examined in contrast with their legitimate news counterparts in 12 contemporary news topics in 4 domains (civics, science, business, and "soft" news). Building on previous work in satire detection, we proposed an SVMbased algorithm, enriched with 5 predictive features (Absurdity, Humor, Grammar, Negative Affect, and Punctuation) and tested their combinations on 360 news articles. Our best predicting feature combination (Absurdity, Grammar and Punctuation) detects satirical news with a 90% precision and 84% recall (F-score=87%). Our work in algorithmically identifying satirical news pieces can aid in minimizing the potential deceptive impact of satire.

- by Sarah Cornwell
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- Discourse Analysis, Human Computer Interaction, Journalism, Art

Traditional approaches to word sense disambiguation (WSD) rest on the assumption that there exists a single, unambiguous communicative intention underlying every word in a document. However, writers sometimes intend for a word to be interpreted as simultaneously carrying multiple distinct meanings. This deliberate use of lexical ambiguity—i.e., punning—is a particularly common source of humour. In this paper we describe how traditional, language-agnostic WSD approaches can be adapted to "disambiguate" puns, or rather to identify their double meanings. We evaluate several such approaches on a manually sense-annotated corpus of English puns and obse

This is an examination of humor, broken into two sections concerned respectively with what we find funny, and why we find things funny at all. I look at various competing theories, comparing them with each other and testing them against a number of pre-theoretical instances of humor, favoring in the end a version of what has been called 'Incongruity-Resolution Theory'. I also take some first steps towards a tentative phenomenology of humor, which may be expanded upon in the future.

- by Christopher DeVeau
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- Philosophy, Philosophy of Mind, Humor (Psychology), Humor

Satire is an attractive subject in deception detection research: it is a type of deception that intentionally incorporates cues revealing its own deceptiveness. Whereas other types of fabrications aim to instill a false sense of truth in the reader, a successful satirical hoax must eventually be exposed as a jest. This paper provides a conceptual overview of satire and humor, elaborating and illustrating the unique features of satirical news, which mimics the format and style of journalistic reporting. Satirical news stories were carefully matched and examined in contrast with their legitimate news counterparts in 12 contemporary news topics in 4 domains (civics, science, business, and “soft” news). Building on previous work in satire detection, we proposed an SVM-based algorithm, enriched with 5 predictive features (Absurdity, Humor, Grammar, Negative Affect, and Punctuation) and tested their combinations on 360 news articles. Our best predicting feature combination (Absurdity, Grammar and Punctuation) detects satirical news with a 90% precision and 84% recall (F-score=87%). Our work in algorithmically identifying satirical news pieces can aid in minimizing the potential deceptive impact of satire. [Note: The associated dataset of the Satirical and Legitimate News, S-n-L News DB 2015-2016, is available via http://victoriarubin.fims.uwo.ca/news-verification/ . The set is password-protected to avoid automated harvesting. Please feel free to request the password, if you are interested.]

- by Victoria L Rubin and +1
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- Human Computer Interaction, Humanities Computing (Digital Humanities), Journalism, Natural Language Processing

Launched in 2013, Vine is a popular microblogging service that allows users to record, edit, and share six-second videos that loop ad libitum, until another video is selected. At this time, the communicative, expressive, and semiotic affordances of Vine and similar services have still to be fully explored by users and scholars alike. Through a multimodal analysis approach drawing on New London Group's (1996) work, this paper investigates how people construct humour on Vine by artfully arranging different modes of expression. The analysis focused on user-enacted humour, as opposed to captured comical scenes or bare samples taken from TV shows or movies. The study hypothesises the social construction of a novel humorous language that draws on extant forms of humour and a variety of modes and techniques derived from audiovisual media and computer-mediated communication, as users inventively exploit the framework provided by the Vine platform. Findings show that users create instant characters to amplify the impact of their solo video recordings, use Vine as a " humorous confessional " , explore the potential of hand-held media by relying on " one hand and face " expressivity (the other hand holding the device for the video " selfie "), and use technology, internet slang, internet acronyms, emoticons/emojis, and hashtags to convey humour and complement the messages of the videos they post on Vine. The goal of this study is an exploratory analysis of humour and its discursive functions in an emergent social medium by considering its affordances, as users find new and creative ways to harness its expressive potential. Keywords: online humour, humour in computer-mediated communication (CMC), multimodal humour, humour on social media, discursive functions of humour.

- by Vittorio Marone
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- Communication, New Media, Social Sciences, Internet Studies

Like its predecessors in 1996 (University of Twente, the Netherlands) and 2002 (ITC-irst, Trento, Italy), this Third International Workshop on Computational Humor (IWCH 2012) focusses on the possibility to find algorithms that allow understanding and generation of humor. There is the general aim of modeling humor, and if we can do that, it will provide us with lots of information about our cognitive abilities in general, such as reasoning, remembering, understanding situations, and understanding conversational partners. But it also provides us with information about being creative, making associations, storytelling and language use. Many more subtleties in face-to-face and multiparty interaction can be added, such as using humor to persuade and dominate, to soften or avoid a face threatening act, to ease a tense situation or to establish a friendly or romantic relationship. One issue to consider is: when is a humorous act appropriate?

- by Gözde Özbal
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- Computational Humor

Lexical polysemy, a fundamental characteristic of all human languages, has long been regarded as a major challenge to machine translation, human–computer interaction, and other applications of computational natural language processing (NLP). Traditional approaches to automatic word sense disambiguation (WSD) rest on the assumption that there exists a single, unambiguous communicative intention underlying every word in a document. However, writers sometimes intend for a word to be interpreted as simultaneously carrying multiple distinct meanings. This deliberate use of lexical ambiguity—i.e., punning—is a particularly common source of humour, and therefore has important implications for how NLP systems process documents and interact with users. In this paper we make a case for research into computational methods for the detection of puns in running text and for the isolation of the intended meanings. We discuss the challenges involved in adapting principles and techniques from WSD to humorously ambiguous text, and outline our plans for evaluating WSD-inspired systems in a dedicated pun identification task. We describe the compilation of a large manually annotated corpus of puns and present an analysis of its properties. While our work is principally concerned with simple puns which are monolexemic and homographic (i.e., exploiting single words which have different meanings but are spelled identically), we touch on the challenges involved in processing other types.

- by Tristan Miller and +1
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- Natural Language Processing, Computational Linguistics, Word Sense Disambiguation, Computational Humor

In this work we provided a contribution in the specific context of verbal humor generation, focused on computational creation of humorous texts. The goal consisted of the design and implementation of a tool for the automatic generation of short humorous expressions. We focused on humorous puns generated through the variation of familiar expressions, performed via lexical substitution. Phonetic and semantic features are employed to select the appropriate substitution. We have chosen a corpus-based approach, in line with a tendency prevailing in the computational linguistics field. A number of textual corpora and dictionaries were employed. We have developed some of these resources (WordNet-Affect and Affective-This thesis could not have been completed without the precious support of many people.

The classic approach to the evaluation of computational humour generators is based on the calculus of the funniness averaged over a random set of generated items. This paper introduces a different approach according to which the key parameter to be evaluated is not the averaged funniness, but the rate of stimuli with a fix value of funniness. The variable employed in the evaluation, called "humorous frequency", was tested on a tool for the generation of a specific class of puns, through the lexical variation of familiar expressions. In particular, the effect of the use of taboo words (e.g. sex words or insults) on the values of humorous frequency was evaluated. The results are a promising first step towards the wider use of humorous frequency in evaluation of computational humour generators.

The inability to quantify key aspects of creative language is a frequent obstacle to natural language understanding. To address this, we introduce novel tasks for evaluating the creativeness of language---namely, scoring and ranking text by humorousness and metaphor novelty. To sidestep the difficulty of assigning discrete labels or numeric scores, we learn from pairwise comparisons between texts. We introduce a Bayesian approach for predicting humorousness and metaphor novelty using Gaussian process preference learning~(GPPL), which achieves a Spearman's~$\rho$ of 0.56 against gold using word embeddings and linguistic features. Our experiments show that given sparse, crowdsourced annotation data, ranking using GPPL outperforms best--worst scaling. We release a new dataset for evaluating humor containing 28,210 pairwise comparisons of 4,030 texts, and make our software freely available.

- by Tristan Miller
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- Natural Language Processing, Metaphor, Computational Humor

This article examines the uses and functions of humour in an online community of gamers and nonprofessional game designers who present and critique user-generated artefacts created with the popular game series LittleBigPlanet. Findings show that participants use humour and “good humour” to achieve a variety of social goals: to veil statements of ability and effort, alleviate negative comments, present user-generated content, attract new players, support other participants, and overall engender a smiling atmosphere that incentives collaboration, peer feedback, and social cohesion. Far from being a trivial ornament, humour emerges as a community building “cushioning glue” that connects, seals, and buffers different gears of computer-mediated interaction, contributing to defining the boundaries and the identity of the analysed online space.

- by Vittorio Marone
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- Discourse Analysis, Communication, Internet Studies, Humor psychology

A pun is a form of wordplay in which a word suggests two or more meanings by exploiting polysemy, homonymy, or phonological similarity to another word, for an intended humorous or rhetorical effect. Though a recurrent and expected feature in many discourse types, puns stymie traditional approaches to computational lexical semantics because they violate their one-sense-per-context assumption. This paper describes the first competitive evaluation for the automatic detection, location, and interpretation of puns. We describe the motivation for these tasks, the evaluation methods, and the manually annotated data set. Finally, we present an overview and discussion of the participating systems' methodologies, resources, and results.

A conversational agent, capable to have a "sense of humour" is presented. The agent can both generate humorous sentences and recognize humoristic expressions introduced by the user during the dialogue. HumoristBot makes use of well founded techniques of computational humor and it has been implemented using the ALICE framework embedded into an Yahoo! Messenger client. It includes also an avatar that changes the face expression according to humoristic content of the dialogue.

- by Agnese Augello and +1
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- Usability, Computational Intelligence, Competitive Intelligence, Complex intelligent systems

In this paper, we focus on humor facilitators, a type of humorous agents meant to act as mediators between unexpected events occurring in smart environments and the human agents. More specifically, we present a case study in which fictional ideation and narrative drama-tization are combined to achieve humor facilitation. We implemented a test bed for the simulation of an interactive environment. Then, we carried out an empirical evaluation with human subjects, aimed to assess the contribution of narrative comments to the humorous effect. The results show first evidence that fictional comments, delivered as dialogue acts, increase the humor response in a statistically significant way.

- by Alessandro Valitutti
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- Computational Humor

We aim to identify and control unintentional humor occurring in human-computer interaction, and recreate it intentionally. In this research we focus on text prediction systems, a type of interactive programs employed in mobile phones, search engines, and word processors. More specifically, we identified two design principles, inspired by humor and emotion theories, and implemented them in a proof-of-concept tool simulating a specific type of text prediction.

- by Alessandro Valitutti
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- Humor, Computational Creativity, Humor Studies, Computational Humor

The translation of wordplay is one of the most extensively researched problems in translation studies, but it has attracted little attention in the fields of natural language processing and machine translation. This is because today's language technologies treat anomalies and ambiguities in the input as things that must be resolved in favour of a single ``correct'' interpretation, rather than preserved and interpreted in their own right. But if computers cannot yet process such creative language on their own, can they at least provide specialized support to translation professionals? In this paper, I survey the state of the art relevant to computational processing of humorous wordplay and put forth a vision of how existing theories, resources, and technologies could be adapted and extended to support interactive, computer-assisted translation.

Abstract: There have been numerous attempts to understand humour's nature and meanings and a few attempts to formalize the sum of this knowledge but the practical aspects of humour recognition and creation have not been given the same level of attention. In a 14 year study of stand-up comedy, social humour, and other humourous forms I have attempted to isolate specific devices that can be utilized in the creation of humour in video games, avatars, robots, and other forms of artificial intelligence. The human experience of humour also involves the frequent repetition of previously experienced humour termed in this paper as "repeatables". A comprehensive use of devices and "repeatables" combined with an understanding of the role of humour in the evolution of human cognition, language, and social systems has the potential to yield an improved ability to entertain, educate, and communicate in digital formats.

- by Christopher Molineux
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- Robotics, Artificial Intelligence, Comedy, HCI

The ongoing work presented here is aimed to investigate to what extent it is possible to perform a feasible use of ambiguous texts in computational humor generation. The first core of a lexical database was developed in order to collect ambiguous terms in the English lexicon. Then an exploratory use of the resource for computational humor generation was performed. Finally, three existing prototypes of humor generator were simulated in order to generate different form of humorous messages from the same lexical resource.

- by Alessandro Valitutti
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- Computational Creativity, Computational Humor

We propose an approach for designing humor facilitation, a specific type of reactive humor consisting in the detection of events and generation of witticisms about them. More specifically, we emphasize the advantage to focus on the negative polarity of events, since this property can be detected with state-of-the-art sentiment analysis and, thus, employed to generate playfully sarcastic comments automatically.

- by Alessandro Valitutti
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- Computational Humor

Condiciones para la evaluación del Curso:

- by ALBERTO LOMBO MONTAÑÉS and +1
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- Humor psychology, Humor (Psychology), Humor, Assessment Of Humor

N atural language's creative genres are traditionally considered to be outside the scope of computational modeling. Computational linguists have paid little attention to humor in particular because it is puzzling by nature. However, given the importance of humor in our daily lives and computers in our work and entertainment, studies related to computational humor will become increasingly significant in fields such as human-computer interaction, intelligent interactive entertainment, and computer-assisted education.

We propose a method for automated generation of adult humor by lexical replacement and present empirical evaluation results of the obtained humor. We propose three types of lexical constraints as building blocks of humorous word substitution: constraints concerning the similarity of sounds or spellings of the original word and the substitute, a constraint requiring the substitute to be a taboo word, and constraints concerning the position and context of the replacement. Empirical evidence from extensive user studies indicates that these constraints can increase the effectiveness of humor generation significantly.

The scientist will set upon the problem like a starved chihuahua on a pork chop."

Language affords a great many opportunities for the intelligent reuse of linguistic content. Rather than always putting our own thoughts into our own words, we often convey feelings through the words of others, by citing, quoting, mimicking, borrowing, varying or ironically echoing what others have already said. Social networking platforms such as Twitter elevate linguistic reuse into an integral norm of digital interaction. On such platforms, who you follow and what you re-tweet can say as much about you as the clothes you wear or the art you hang on your walls. But not everyone that is worth following is human, and not everything that is worth re-tweeting was first coined by a real person. More and more of the witty and thought-provoking content on Twitter is generated by bots, artificial systems that write their own material and vie for our attention just as humans do. Real people knowingly follow artificial bots for reasons that are subtle and diverse, but a significant reason i...

- by Tony Veale
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- Computational Creativity, Computational Humor

Humor is one of the most interesting and puzzling aspects of human behavior. Despite the attention it has received in fields such as philosophy, linguistics, and psychology, there have been only few attempts to create computational models for humor recognition or generation. In this article, we bring empirical evidence that computational approaches can be successfully applied to the task of humor recognition. Through experiments performed on very large data sets, we show that automatic classification techniques can be effectively used to distinguish between humorous and non-humorous texts, with significant improvements observed over a priori known baselines.

We consider automated generation of humorous texts by substitution of a single word in a given short text. In this setting, several factors that potentially contribute to the funniness of texts can be integrated into a unified framework as constraints on the lexical substitution. We discuss three types of such constraints: formal constraints concerning the similarity of sounds or spellings between the original word and the substitute, semantic or connotational constraints requiring the substitute to be a taboo word, and contextual constraints concerning the position and context of the replacement. Empirical evidence from extensive user studies using real SMSs as the corpus indicates that taboo constraints are statistically very effective, and so is a constraint requiring that the substitution takes place at the end of the text even though the effect is smaller. The effects of individual constraints are largely cumulative. In addition, connotational taboo words and word position have a strong interaction.

- by Alessandro Valitutti
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- Computational Creativity, Computational Humor

In this paper, we focus on humor facilitators, a type of humorous agents meant to act as mediators between unexpected events occurring in smart environments and the human agents. More specifically, we present a case study in which fictional ideation and narrative dramatization are combined to achieve humor facilitation. We implemented a test bed for the simulation of an interactive environment. Then, we carried out an empirical evaluation with human subjects, aimed to assess the contribution of narrative comments to the humorous effect. The results show first evidence that fictional comments, delivered as dialogue acts, increase the humor response in a statistically significant way.

- by Tony Veale
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- Computational Humor
- by La Biblioteca Giuridica and +3
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- Criminal Procedure, Comparative Law, English Literature, Humor psychology

This paper describes the system developed
for SemEval 2017 task 6: #HashTagWars -
Learning a Sense of Humor. Learning to
recognize sense of humor is the important
task for language understanding applications.
Different set of features based on
frequency of words, structure of tweets
and semantics are used in this system to
identify the presence of humor in tweets.
Supervised machine learning approaches,
Multilayer perceptron and Naïve Bayes are
used to classify the tweets in to three levels
of sense of humor. For given Hashtag,
the system finds the funniest tweet and
predicts the amount of funniness of all the
other tweets. In official submitted runs, we
have achieved 0.506 accuracy using multilayer
perceptron in subtask-A and 0.938
distance in subtask-B. Using Naïve bayes
in subtask-B, the system achieved 0.949
distance. Apart from official runs, this system
have scored 0.751 accuracy in subtask-A
using SVM.

The New Yorker publishes a weekly captionless cartoon. More than 5,000 readers submit captions for it. The editors select three of them and ask the readers to pick the funniest one. We describe an experiment that compares a dozen automatic methods for selecting the funniest caption. We show that negative sentiment, human-centeredness, and lexical centrality most strongly match the funniest captions, followed by positive sentiment. These results are useful for understanding humor and also in the design of more engaging conversational agents in text and multimodal (vision+text) systems. As part of this work, a large set of cartoons and captions is being made available to the community.

- by Paloma de Juan
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- Creativity, Computational Humor, Collective Discourse

The creation of advertising messages is a deep process of creative writing production. As far as the textual content is concerned, there are not many computational tools (besides the usual dictionaries, thesauri or program for performing of simple wordplays) that help the copywriter activity. In this work we explore the use of natural language processing techniques for proposing solutions to advertising professionals and improving the quality of advertising messages, opening up the way to a full automatization of the whole process. In the proposed system, we consider two steps: (i) the creative variation of familiar expressions , taking into account the affective content of the produced text, (ii) the automatic animation (semantically consistent with the affective text content) of the resulting expression, using kinetic typography techniques.

- by Alessandro Valitutti
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- Computational Creativity, Computational Humor

- by Alessandro Valitutti
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- Computational Creativity, Computational Humor
- by Vittorio Marone
- •
- Discourse Analysis, Communication, Internet Studies, Humor psychology

This paper describes the system developed for SemEval 2017 task 6: #HashTagWars-Learning a Sense of Humor. Learning to recognize sense of humor is the important task for language understanding applications. Different set of features based on frequency of words, structure of tweets and semantics are used in this system to identify the presence of humor in tweets. Supervised machine learning approaches, Multilayer perceptron and Naïve Bayes are used to classify the tweets in to three levels of sense of humor. For given Hashtag, the system finds the funniest tweet and predicts the amount of funniness of all the other tweets. In official submitted runs, we have achieved 0.506 accuracy using multilayer perceptron in subtask-A and 0.938 distance in subtask-B. Using Naïve bayes in subtask-B, the system achieved 0.949 distance. Apart from official runs, this system have scored 0.751 accuracy in subtask-A using SVM.

- by Reenie Mahajan
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- Computer Science, Natural Language Processing, Computational Humor, Deep Learning