This talk will analyze responses to funniness of five versions of 10 different jokes. The responses of one of them will then be compared to theoretical analysis and representation of the same joke based on Script-based Semantics Theory of Humor, General Theory of Verbal Humor, and Ontological Semantic Theory of Humor.

In this article some formal, content-related and procedural considerations towards the sense of humor are articulated and the analysis of both everyday humor behavior and of comic styles leads to the initial proposal of a four factor-model of humor (4FMH). This model is tested in a new dataset and it is also examined whether two forms of comic styles (benevolent humor and moral mockery) do fit in. The model seems to be robust but further studies on the structure of the sense of humor as a personality trait are required.

This exercise in metatheory presents what any theory consists of and what properties it should have. It, then, adjust the general recipe to a theory of humor and computational humor. In this light, it reviews the state of the art in computational humor and suggests the main lines of development.

Based on recent experiences between a laughing virtual agent and a human user at the intersection AI and humor and laughter, this paper aims to highlight some of the psychological considerations, when conducting AI and humor experiments. The systematic and standardized approach outlined in this paper will demonstrate how to reduce error variance that may be caused by confound variables such as having poor experimental controls. From the necessity of cover stories, protocols and procedures, the differences to the pros and cons of measuring subjectively and objectively and what is required so that both give valid and reliable results are offered as solutions to achieving this goal. Furthermore, the psychological individual differences that need consideration, such as the appreciation of different types of humor, mood, personality variables, for example, trait and state cheerfulness, and gelotophobia- the fear of being laughed at are discussed.

The fable of Hansel and Gretel describes the plight of two children over two types of threat; harm to their immediate survival and pain from hunger. The two contexts of self-preservation and feeding are evident from the flow of the story dialog, therefore an automatic re-playing of dialog can be realized by picking sentences from two lists; one containing sentences in the context of self-preservation, the other in the context of feeding. Theory and Internet humor appreciation surveys suggest that humorous sentences in the context of self-preservation have relatively constant preference with respect to age, while in the context of hunger and protection of feeding turf to decline with age, reflecting the reduced need for food with aging. Sentences in the context of sociosexual relationships increased in preference until adulthood then declined with maturity. Also, sentences in parenting context, such as when caring for offspring, society and the environment were found to increase in preference with age and maturity. Therefore in order to construct a recursive Hansel and Gretel dialog for audience of all ages, two lists of sentences are added to feeding: In sociosexual and parenting contexts. The self-preservation list is paired with one of the remaining three, representing three stages of age; youth, adulthood and maturity. The single thread story of Hansel and Gretel serves as a template for recursive dialog, making it possible to create alternative threads and unbound possibilities for plots, thereby duplicating the story structure without repeating the narrative.

Patients with schizophrenia are characterized by humor recognition deficit which is connected with their cognitive disorder such as inability to filter out irrelevant stimuli. As soon as patients with schizotypal and affective disorders easily recognize humor, this may be used as a strong diagnostic criterion in clinical practice. On the other hand humor recognition by artificial intellect became a hot question in computer science in a flow of attempts to bring human-computer communication closer to social. It is argued that schizophrenic and computer thinking have common features. Both have lack of social and emotional context understanding. To compare failures in humor recognition made by patients with schizophrenia vs computer may move forward theory and practice of both clinical psychology and computer science.

In English, “puns” are usually perceived as a subclass of “jokes”. In Japanese, however, this is not necessarily true. In this paper we investigate whether Japanese native speakers perceive dajare (puns) as jooku (jokes). We first summarize existing research in the field of computational humor, both in English and Japanese, focusing on the usage of these two terms. This shows that in works of Japanese native speakers, puns are not commonly treated as jokes. Next we present some dictionary definitions of dajare and jooku, which show that they may actually be used in a similar manner to English. In order to study this issue, we conducted a survey, in which we asked Japanese participants three questions: whether they like jokes (jooku), whether they like puns (dajare) and whether dajare are jooku. The results showed that there is no common agreement regarding dajare being a genre of jokes. We analyze the outcome of this experiment and discuss them from different points of view.

This research is a discriminative analysis of conversational dialogs involving individuals suffering from dementia of Alzheimer’s type. Several metric analyses are applied to the transcripts of the Carolina Conversation Corpus (Pope and Davis 2011) in order to determine if there are significant statistical differences between individuals with and without Alzheimer’s disease. Results from the analysis indicate that go-ahead utterances, certain fluency measures, and paraphrasing provide defensible means of differentiating the linguistic characteristics of spontaneous speech between healthy individuals and those with Alzheimer’s disease. Several machine learning algorithms were used to classify the speech of individuals with and without dementia of the Alzheimer’s type.

Relational data representations have become an increasingly important topic due to the recent proliferation of network datasets (e.g., social, biological, information networks) and a corresponding increase in the application of Statistical Relational Learning (SRL) algorithms to these domains. In this article, we examine and categorize techniques for transforming graph-based relational data to improve SRL algorithms. In particular, appropriate transformations of the nodes, links, and/or features of the data can dramatically affect the capabilities and results of SRL algorithms. We introduce an intuitive taxonomy for data representation transformations in relational domains that incorporates link transformation and node transformation as symmetric representation tasks. More specifically, the transformation tasks for both nodes and links include (i) predicting their existence, (ii) predicting their label or type, (iii) estimating their weight or importance, and (iv) systematically constructing their relevant features. We motivate our taxonomy through detailed examples and use it to survey competing approaches for each of these tasks. We also discuss general conditions for transforming links, nodes, and features. Finally, we highlight challenges that remain to be addressed.

Dual decomposition, and more generally Lagrangian relaxation, is a classical method for combinatorial optimization; it has recently been applied to several inference problems in natural language processing (NLP). This tutorial gives an overview of the technique. We describe example algorithms, describe formal guarantees for the method, and describe practical issues in implementing the algorithms. While our examples are predominantly drawn from the NLP literature, the material should be of general relevance to inference problems in machine learning. A central theme of this tutorial is that Lagrangian relaxation is naturally applied in conjunction with a broad class of combinatorial algorithms, allowing inference in models that go significantly beyond previous work on Lagrangian relaxation for inference in graphical models.