DOCSLIB.ORG

Automatically Identifying Quirky Scientific

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Automatically Identifying Quirky Scientific How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements Chen Shani∗, Nadav Borenstein∗, Dafna Shahaf The Hebrew University of Jerusalem fchenxshani, nadav.borenstein, [email protected] Abstract Nevertheless, computational humor remains a Humor is an important social phenomenon, long-standing challenge in AI; It requires complex serving complex social and psychological language understanding, manipulation capabilities, functions. However, despite being studied for creativity, common sense, and empathy. Some even millennia humor is computationally not well claim that computational humor is an AI-complete understood, often considered an AI-complete problem (Stock and Strapparava, 2002). problem. As humor is a broad phenomenon, most works In this work, we introduce a novel setting in on computational humor focus on specific humor humor mining: automatically detecting funny types, such as knock-knock jokes or one-liners (Mi- and unusual scientific papers. We are inspired halcea and Strapparava, 2006; Taylor and Mazlack, by the Ig Nobel prize, a satirical prize awarded annually to celebrate funny scientific achieve- 2004). In this work, we present a novel humor ments (example past winner: “Are cows more recognition task: identifying quirky, funny scien- likely to lie down the longer they stand?”). tific contributions. We are inspired by the Ig Nobel This challenging task has unique characteris- prize1, a satiric prize awarded annually to ten sci- tics that make it particularly suitable for auto- entific achievements that “first make people laugh, matic learning. and then think”. Past Ig Nobel winners include We construct a dataset containing thousands “Chickens prefer beautiful humans” and “Beauty of funny papers and use it to learn classifiers, is in the eye of the beer holder: People who think combining findings from psychology and lin- they are drunk also think they are attractive”. guistics with recent advances in NLP. We use our models to identify potentially funny papers Automatically identifying candidates for the Ig in a large dataset of over 630,000 articles. The Nobel prize provides a unique perspective on hu- results demonstrate the potential of our meth- mor. Unlike most humor recognition tasks, the ods, and more broadly the utility of integrat- humor involved is sophisticated, and requires com- ing state-of-the-art NLP methods with insights mon sense, as well as specialized knowledge and from more traditional disciplines. understanding of the scientific culture. On the other 1 Introduction hand, this task has several characteristics rendering it attractive: the funniness of the paper can often Humor is an important aspect of the way we inter- be recognized from its title alone, which is short, arXiv:2106.03048v1 [cs.CL] 6 Jun 2021 act with each other, serving complex social func- with simple syntax and no complex narrative struc- tions (Martineau, 1972). Humor can function either ture (as opposed to longer jokes). Thus, this is a as a lubricant or as an abrasive: it can be used as a relatively clean setting to explore our methods. key for improving interpersonal relations and build- We believe humor in science is also particularly ing trust (Wanzer et al., 1996; Wen et al., 2015), or interesting to explore, as humor is strongly tied to help us work through difficult topics. It can also aid creativity. Quirky contributions could sometimes in breaking taboos and holding power to account. indicate fresh perspectives and pioneering attempts Enhancing the humor capabilities of computers has to expand the frontiers of science. For example, tremendous potential to better understand interac- Andre Geim won an Ig Nobel in 2000 for levitating tions between people, as well as build more natural a frog using magnets and a Nobel Prize in Physics human-computer interfaces. ∗Equal contribution 1improbable.com/ig-about in 2010. The Nobel committee explicitly attributed violation of expectations. Example Ig Nobel win- the win to his playfulness (The Royal Swedish ners include: “Will humans swim faster or slower Academy of Science, 2010). in syrup?” and ”Coordination modes in the multi- Our contributions are: segmental dynamics of hula hooping”. • We formulate a novel humor recognition task The second category, sex-related humor is also in the scientific domain. common among Ig Nobel winning papers. Exam- • We construct a dataset containing thousands ples include: “Effect of different types of textiles of funny scientific papers. on sexual activity. Experimental study” and “Mag- • We develop multiple classifiers, combining netic resonance imaging of male and female geni- findings from psychology and linguistics with tals during coitus and female sexual arousal”. recent NLP advances. We evaluate them both Humor Detection in AI. Most computational hu- on our dataset and in a real-world setting, iden- mor detection work done in the context of AI relies tifying potential Ig Nobel candidates in a large on supervised or semi-supervised methods and fo- corpus of over 0:6M papers. cuses on specific, narrow, types of jokes or humor. • We devise a rigorous, data-driven way to ag- Humor detection is usually formulated as a bi- gregate crowd workers’ annotations for sub- nary text classification problem. Example domains jective questions. include knock-knock jokes (Taylor and Mazlack, 2 • We release data and code . 2004), one-liners (Miller et al., 2017; Simpson Beyond the tongue-in-cheek nature of our ap- et al., 2019; Liu et al., 2018; Mihalcea and Strappa- plication, we more broadly wish to promote com- rava, 2005; Blinov et al., 2019; Mihalcea and Strap- bining data-driven research with more-traditional parava, 2006), humorous tweets (Maronikolakis works in areas such as psychology. We believe in- et al., 2020; Donahue et al., 2017; Ortega-Bueno sights from such fields could complement machine et al., 2018; Zhang and Liu, 2014), humorous prod- learning models, improving performance as well uct reviews (Ziser et al., 2020; Reyes and Rosso, as enriching our understanding of the problem. 2012), TV sitcoms (Bertero and Fung, 2016), short stories (Wilmot and Keller, 2020), cartoons cap- 2 Related Work tions (Shahaf et al., 2015), and even “That’s what she said” jokes (Hossain et al., 2017; Kiddon and Humor in the Humanities. A large body of theo- Brun, 2011). Related tasks such as irony, sarcasm retical work on humor stems from linguistics and and satire have also been explored in similarly nar- psychology. Ruch(1992) divided humor into three row domains (Davidov et al., 2010; Reyes et al., categories: incongruity, sexual, and nonsense (and 2012; Pta´cekˇ et al., 2014). created a three-dimensional humor test to account for them). Since our task is to detect humor in 3 Problem Formulation and Dataset scientific contributions, we believe that the third category can be neglected under the assumption Our goal in this paper is to automatically identify that no-nonsense article would (or at least, should) candidates for the Ig Nobel prize. More precisely, be published (notable exception: the Sokal hoax to automatically detect humor in scientific papers. (Sokal, 1996)). First, we consider the question of input to our The first category, incongruity, was first fully algorithm. Sagi and Yechiam(2008) found a strong conceptualized by Kant in the eighteenth century correlation between funny title and humorous sub- (Shaw, 2010). The well-agreed extensions to in- ject in scientific papers. Motivated by this correla- congruity theory are the linguistics incongruity res- tion, we manually inspected a subset of Ig Nobel olution model and semantic script theory of humor winners. For the vast majority of them, reading the (Suls, 1972; Raskin, 1985). Both state that if a title was enough to determine whether it is funny; situation ended in a manner that contradicted our very rarely did we need to read the abstract, let prediction (in our case, the title contains an unex- alone the full paper. Typical past winners’ titles in- pected term) and there exists a different, less likely clude “Why do old men have big ears?” and “If you rule to explain it – the result is a humorous expe- drop it, should you eat it? Scientists weigh in on the rience. Simply put, the source of humor lies in 5-second rule”. An example of a non-informative title is “Pouring flows”, a paper calculating the 2github.com/nadavborenstein/Iggy optimal way to dunk a biscuit in a cup of tea. Based on this observation, we decided to focus N-gram Based LMs. We train simple N-gram on the papers’ titles. More formally: Given a title t LMs with n 2 f1; 2; 3g on two corpora – 630,000 of an article, our goal is to learn a binary function titles from Semantic Scholar, and 231,600 one-line '(t) ! f0; 1g, reflecting whether the paper is hu- jokes (Moudgil, 2016). morous, or ‘Ig Nobel-worthy’. The main challenge, Syntax-Based LMs. Here we test the hypothesis of course, lies in the construction of '. that humorous text has more surprising grammati- To take a data-driven approach to tackle this cal structure (Oaks, 1994). We replace each word problem, we crafted a first-of-its-kind dataset con- in our Semantic Scholar corpus with its correspond- taining titles of funny scientific papers2. We started ing part-of-speech (POS) tag6. We then trained from the 211 Ig Nobel winners. Next, we manually N-gram based LMs (n 2 f1; 2; 3g) on this corpus. collected humorous papers from online forums and Transformer-Based LMs. We use three different blogs3, resulting in 1,707 papers. We manually Transformers based (Vaswani et al., 2017) mod- verified all of these papers can be used as positive els: 1) BERT (Devlin et al., 2018) (pre-trained on examples. In Section6 we give more indication Wikipedia and the BookCorpus), 2) SciBERT (Belt- these papers are indeed useful for our task.
Load more

Recommended publications
  • Proceedings of 3Rd International Workshop on Computational Humor
    Computational Humor 2012 EXTENDED ABSTRACTS OF THE (3rd INTERNATIONAL) WORKSHOP ON COMPUTATIONAL HUMOR Amsterdam, June 8, 2012 Anton Nijholt (ed.) CIP GEGEVENS KONINKLIJKE BIBLIOTHEEK, DEN HAAG Nijholt, A. Computational Humor 2012 Proceeding of 3rd International Workshop on Computational Humor A. Nijholt (ed.) Amsterdam, Universiteit Twente, Faculteit Elektrotechniek, Wiskunde en Informatica ISSN 0929–0672 CTIT Workshop Proceedings Series WP12-02 Keywords: humor, humor models, humor theory, humor generation, corpora, jokes, semantics ontologies, natural language processing © Copyright 2012; Universiteit Twente, Enschede Book orders: Ms. C. Bijron University of Twente Faculty of Electrical Engineering, Mathematics and Computer Science P.O. Box 217 NL 7500 AE Enschede tel: +31 53 4893740 fax: +31 53 4893503 Email: [email protected] Druk- en bindwerk: Ipskamp Drukkers, Enschede. Preface to Computational Humor 2012 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.
    [Show full text]
  • Computational Humor [email protected] Kim Binsted, University of Hawaii
    Trends & Controversies Steven Staab University of Karlsruhe Computational Humor [email protected] Kim Binsted, University of Hawaii No, this is no April Fool’s prank. Computer scientists at labs around the world are conducting serious research into … humor. Although it might seem whimsical, many excellent reasons exist to take a closer look at this fascinating aspect of human cognition and interaction. cally addressed language phenomena that are amenable to Humor affects attention and memory, facilitates social interaction, combinatorial approaches using static and stereotypical and ameliorates communication problems. If computers are ever semantic representations. Although such approaches are going to communicate naturally and effectively with humans, they adequate for much of language, they’re not easily extended must be able to use humor. Moreover, humor provides insight into to capture humans’ more creative language interpretation how humans process language—real, complex, creative language, capacities. An alternative tack is to begin by modeling less not just a tractable subset of standard sentences. By modeling humor typical, more complex phenomena, with the goal of generation and understanding on computers, we can gain a better encompassing standard language as a trivial case.1 picture of how the human brain handles not just humor but language and cognition in general. Semantic interpretation In popular perception, computers will never be able to use or Suppose you hear someone saying, “Everyone had so appreciate humor. Fictional computers and robots are almost always much fun diving from the tree into the swimming pool, we portrayed as humorless, even if they’re skilled at natural language, decided to put in a little … ” At the point in the sentence graceful bipedal motion, and other human behaviors that current where you hear the words “put in,” you’ve already com- machines find challenging.
    [Show full text]
  • Computational Creativity: Coming of Age
    Editorial Computational Creativity: Coming of Age Simon Colton, Ramon López de Mántaras, and Oliviero Stock n This editorial provides an introduction to current AI research on computationally created A artifacts as well as to the papers and topics cov- t its heart, computational creativity is the study of build- ered by this special issue on computational cre- ing software that exhibits behavior that would be deemed cre- ativity. ative in humans. Such creative software can be used for autonomous creative tasks, such as inventing mathematical the- ories, writing poems, painting pictures, and composing music. However, computational creativity studies also enable us to understand human creativity and to produce programs for cre- ative people to use, where the software acts as a creative collab- orator rather than a mere tool. Historically, it’s been difficult for society to come to terms with machines that purport to be intel- ligent and even more difficult to admit that they might be cre- ative. For instance, in 1934, some professors at the University of Manchester in the United Kingdom built meccano models that were able to solve some mathematical equations. Groundbreak- ing for its time, this project was written up in a piece in Mecca- no Magazine. The article was titled “Are Thinking Machines Pos- sible” and was very upbeat, but surprisingly ends by stating that “Truly creative thinking of course will always remain beyond the power of any machine.” Surely, though, this attitude has changed in light of the amaz- ing advances in hardware and software technology that fol- lowed those meccano models? Sadly, no.
    [Show full text]
  • Learning to Laugh (Automatically): Computational Models for Humor
    Computational Intelligence, Volume 22, Number 2, 2006 LEARNING TO LAUGH (AUTOMATICALLY): COMPUTATIONAL MODELS FOR HUMOR RECOGNITION RADA MIHALCEA Department of Computer Science, University of North Texas, Denton, TX 76203 CARLO STRAPPARAVA ITC – irst, Istituto per la Ricerca Scientifica e Tecnologica, I-38050, Povo, Trento, Italy 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 compu- tational 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. Key words: computational humor, humor recognition, sentiment analysis, one-liners. 1. INTRODUCTION ... pleasure has probably been the main goal all along. But I hesitate to admit it, because computer scientists want to maintain their image as hard-working individuals who deserve high salaries. Sooner or later society will realize that certain kinds of hard work are in fact admirable even though they are more fun than just about anything else. (Knuth 1993) Humor is an essential element in personal communication. While it is merely consid- ered a way to induce amusement, humor also has a positive effect on the mental state of those using it and has the ability to improve their activity. Therefore computational humor deserves particular attention, as it has the potential of changing computers into a creative and motivational tool for human activity (Stock, Strapparava, and Nijholt 2002; Nijholt et al.
    [Show full text]
  • Computational Humor
    Making Computers Laugh: Investigations in Automatic Humor Recognition Rada Mihalcea Carlo Strapparava Department of Computer Science Istituto per la Ricerca Scientifica e Tecnologica University of North Texas ITC – irst Denton, TX, 76203, USA I-38050, Povo, Trento, Italy [email protected] [email protected] Abstract Previous work in computational humor has fo- cused mainly on the task of humor generation (Stock Humor is one of the most interesting and and Strapparava, 2003; Binsted and Ritchie, 1997), puzzling aspects of human behavior. De- and very few attempts have been made to develop spite the attention it has received in fields systems for automatic humor recognition (Taylor such as philosophy, linguistics, and psy- and Mazlack, 2004). This is not surprising, since, chology, there have been only few at- from a computational perspective, humor recogni- tempts to create computational models for tion appears to be significantly more subtle and dif- humor recognition or generation. In this ficult than humor generation. paper, we bring empirical evidence that In this paper, we explore the applicability of computational approaches can be success- computational approaches to the recognition of ver- fully applied to the task of humor recogni- bally expressed humor. In particular, we investigate tion. Through experiments performed on whether automatic classification techniques are a vi- very large data sets, we show that auto- able approach to distinguish between humorous and matic classification techniques can be ef- non-humorous text, and we bring empirical evidence fectively used to distinguish between hu- in support of this hypothesis through experiments morous and non-humorous texts, with sig- performed on very large data sets.
    [Show full text]
  • XSYSIGMA at Semeval-2020 Task 7: Method for Predicting Headlines
    XSYSIGMA at SemEval-2020 Task 7: Method for Predicting Headlines’ Humor Based on Auxiliary Sentences with EI-Bert Jian Ma , Shu-Yi Xie , Mei-Zhi Jin, Lian-Xin Jiang , Yang Mo , Jian-Ping Shen Ping An Life Insurance AI Department ShenZhen, China fMAJIAN446,XIESHUYI542,EX-JINMEIZHI001,JIANGLIANXIN769, MOYANG853,[email protected] Abstract This paper describes xsysigma team’s system for SemEval 2020 Task 7: Assessing the Funniness of Edited News Headlines. The target of this task is to assess the funniness changes of news headlines after minor editing and is divided into two subtasks: Subtask 1 is a regression task to detect the humor intensity of the sentence after editing; and Subtask 2 is a classification task to predict funnier of the two edited versions of an original headline. In this paper, we only report our implement of Subtask 2. We first construct sentence pairs with different features for Enhancement Inference Bert(EI-Bert)’s input. We then conduct data augmentation strategy and Pseudo-Label method. After that, we apply feature enhancement interaction on the encoding of each sentence for classification with EI-Bert. Finally, we apply weighted fusion algorithm to the logits results which obtained by different pre-trained models. We achieve 64.5% accuracy in subtask2 and rank the first and the fifth in dev and test dataset1, respectively. 1 Introduction Humor detection is a significant task in natural language processing. Most of the available humor datasets treat it as a binary classification task (Khodak et al., 2017; Davidov et al., 2010; Barbieri et al., 2014), i.e., examining whether a particular text is funny.
    [Show full text]
  • Exploring Mainstream Natural Language Processing
    EXPLORING MAINSTREAM NATURAL LANGUAGE PROCESSING TECHNIQUES AND THEIR APPLICATION TO COMPUTATIONAL HUMOR An Undergraduate Research Scholars Thesis by DHANANJAY KHANNA Submitted to the Undergraduate Research Scholars program at Texas A&M University in partial fulfillment of the requirements for the designation as an UNDERGRADUATE RESEARCH SCHOLAR Approved by Research Advisor: Dr. Dylan Shell May 2018 Major: Computer Science TABLE OF CONTENTS Page ABSTRACT .................................................................................................................................. 1 ACKNOWLEDGMENTS ............................................................................................................ 2 NOMENCLATURE ..................................................................................................................... 3 CHAPTER I. INTRODUCTION ...................................................................................................... 4 II. METHODS ................................................................................................................. 9 Generation ........................................................................................................... 11 Representation ..................................................................................................... 12 III. RESULTS ................................................................................................................. 16 Generation ..........................................................................................................
    [Show full text]
  • Computers Learning Humor Is No Joke
    Harvard Data Science Review Computers Learning Humor Is No Joke Thomas Winters Published on: Apr 30, 2021 DOI: 10.1162/99608f92.f13a2337 License: Creative Commons Attribution 4.0 International License (CC-BY 4.0) Harvard Data Science Review Computers Learning Humor Is No Joke Column Editor’s note: Machine generation and comprehension of language is a difficult enough task, but it's not even funny how difficult it is for a machine to produce and detect humor. Data scientist Thomas Winters explains the reasons for this challenge, and addresses how much humor a computer can learn. (Spoiler: A bit). Keywords: computational humor, humor detection, humor generation Can a computer have a sense of humor? On reflection, this question may seem paradoxical given that humor is such an intrinsically human trait. While some primates produce laughter (Gervais & Wilson, 2005; Preuschoft & Hooff, 1997), humans are the only known species that use humor for making others laugh. Furthermore, every known human civilization also has had at least some form of humor for making others laugh (Caron, 2002; (Gervais & Wilson, 2005. Given this innately and intrinsically human skill, it might seem implausible for a machine to have any form of a sense of humor. In this article, we explore several computational techniques that have been used to detect and generate humor, and how these systems can help us improve our humor abilities and gain more insights into this uniquely human trait. Human Sense of Humor Over the whole of human history, many scholars investigated the inner workings of humor, resulting in an abundance of different theories for explaining verbally expressed humor.
    [Show full text]
  • An Evocative Humorist Chat-Bot
    Mobile Information Systems 4 (2008) 165–181 165 IOS Press EHeBby: An evocative humorist chat-bot Giovanni Pilatoa,∗, Agnese Augellob, Giorgio Vassallob and Salvatore Gaglioa,b aICAR-CNR Istituto di CAlcolo e Reti ad alte prestazioni, Italian National Research Council, Viale delle Scienze, Ed. 11, 90128 Palermo, Italy bDINFO – Dipartimento di Ingegneria Informatica, University of Palermo, Viale delle Scienze, Ed. 6, 90128 Palermo, Italy Abstract. A conversational agent, capable to have a “sense of humor” is presented. The agent can both generate humorous sentences and recognize humoristic expressions introduced by the user during the dialogue. EHeBby is an entertainment oriented conversational agent implemented using the ALICE framework embedded into an Yahoo! Messenger client. It is characterized by two areas: a rational, rule-based area and an evocative area. The first one is based on well founded techniques of computational humor and a standard AIML KB. The second one is based on a conceptual space, automatically induced by a corpus of funny documents, where KB items and user sentences are mapped. This area emulates an associative/evocative behavior of the conversational agent, making her more attractive. EHeBby includes also an avatar that changes the face expression according to humoristic content of the dialogue. 1. Introduction The interest about enhancing the interface usability of applications and entertainment platforms has increased in last years. Intelligent user interfaces can help people during the interaction with a system in a natural manner, trying to understand and anticipate user needs [22]. Pattern matching, finite-state-machines and frame-based models are commonly used as methodologies for designing chat-bots.
    [Show full text]
  • Computer, Tell Me a Joke ... but Please Make It Funny: Computational Humor with Ontological Semantics
    Computer, Tell Me a Joke ... but Please Make it Funny: Computational Humor with Ontological Semantics Christian F. Hempelmann1, Victor Raskin2, and Katrina E. Triezenberg2 Georgia Southern University1 Purdue University2 Department of Writing and Linguistics NLP Lab and Linguistics Program Statesboro, GA 30460 West Lafayette, IN 47905 [email protected] {vraskin, kattriez}@purdue.edu Abstract based human-computer interaction. Finally, an additional Computational humor is a subdiscipline of computational benefit of studying computational humor can be reaped for linguistics with applications in human computer interfaces, those interested in humor, as it requires formalization of edutainment, affective computing, intelligent agents, and the key components in order to make them palatable to the other areas. Based on ontological semantics, we develop the dumb machine. resources and algorithms the computer needs to understand and produce humor, in principle and on a detailed example. Before and since Apple’s joke teller, little progress has Our ultimate output will not be that of a toy system that fills been made in computational humor, both in humor analysis in templates, previously the only available approach, but and humor generation (HG), and we predict that extant rather true natural language generation, based on the template-based systems are not going to lead to computer approximation of the human understanding of the joke. This paper shows how ontological semantics provides breakthroughs in natural language engineering, and much for and computes the full computer understanding of humor, less in forcing us to create theories that could tell us about a sine qua non of humor generation. the mechanisms involved in human humor use.
    [Show full text]
  • Analysis and Generation of Humorous Texts in Hindi
    Computational Humour: Analysis and Generation of Humorous Texts in Hindi Thesis submitted in partial fulfillment of the requirements for the degree of Masters of Science in Computational Linguistics by Research by Srishti Aggarwal 201325049 [email protected] International Institute of Information Technology Hyderabad - 500 032, INDIA October 2018 Copyright c Srishti Aggarwal, 2018 All Rights Reserved International Institute of Information Technology Hyderabad, India CERTIFICATE It is certified that the work contained in this thesis, titled “Computational Humour: Analysis and Gener- ation of Humorous Texts in Hindi” by Srishti Aggarwal, has been carried out under my supervision and is not submitted elsewhere for a degree. Date Adviser: Prof. Radhika Mamidi To My Parents Acknowledgments I am grateful to all the wonderful people in my life for their encouragement and support for my research work. First and foremost, I thank my parents for their love, support and faith in me. Thank you for being there for me, and for always getting me last minute tickets whenever I felt like coming home. I thank my sister for always wishing me well and being my silent supporter. I thank my family - my grandparents, uncles, aunts, cousins - for celebrating the smallest of victories, encouraging me in the biggest of defeats, and for keeping a joke running about the topic of my research. I would like to express my gratitude towards my advisor Prof. Radhika Mamidi who guided and supported me at each step of this work, right from when I showed interest in this topic more than two years ago. She showed great patience in giving me the freedom to work on my own pace, while also keeping me grounded and on track.
    [Show full text]
  • Computational Creativity: Automated Pun Generation
    International Journal of Computer Applications (0975 – 8887) Volume 140 – No.10, April 2016 Computational Creativity: Automated Pun Generation Priyanshi R. Shah Chintan D. Thakkar Swati Mali K.J. Somaiya K.J. Somaiya Department of College of Engineering College of Engineering Computer Engineering Ghatkopar, Mumbai-77 Ghatkopar, Mumbai-77 K.J. Somaiya Maharashtra India Maharashtra India College of Engineering Ghatkopar, Mumbai-77 Maharashtra India ABSTRACT puns (riddles in question answer format) using richness of Humor is an integral part of our day-to-day communication English language and concepts like synonyms, homophones to making it interesting and plausible. The growing demand in help children build their vocabulary in a fun-loving way. It robotics and the constant urge for machines to pass the Turing could also be used by old retired people as a source of test to eliminate the thin line difference between human and entertainment. machine behavior make it essential for machines to be able to There has been immense research in the field of use humor in communication. computational creativity since 1992 and the JAPE Moreover, Learning is a continuous process and very algorithm[10], the only successful prototype implementation important at every stage of life. However sometimes merely proving humor could be used by machines too was reading from a book induces lassitude and lack of revolutionary. However there was no practical application of concentration may hamper strong foundation building. the prototype created as it lacked a proper GUI and the puns generated were quite often bizarre. Children suffering from Autism Spectrum Disorder (ASD) suffer from slow learning and grasping issues.
    [Show full text]