This extended abstract presents a work in progress of using terminological resources from the biomedical domain to systematically study the change of domain terminology over time. In particular we investigate term replacement. In order to study term replacement over time, semantic knowledge like conceptual granularity of a term is necessary. We analyze three popular biomedical terminology resources (UMLS, CTD, SNOMED CT) and show how information provided there can be used to extract lexically distinctive synonym sets that exclude variants. We use the entire PubMed dataset to chronologically study occurrences of extracted synonyms. Our experiments on the disease subsets of three terminologies reveal that the phenomenon of term replacement can be observed in around 60% of the extracted synonym sets.

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.

In the current generation The main results issued from our first experiments (Legras of UV Systems, several ground operators operate a single et al. 2008; Coppin and Legras 2012) were that the swarm vehicle with limited autonomous capabilities, whereas, approach seemed to be robust and adapted for simple mission in the next generation of UV Systems, a ground operator of surveillance, but that the operators in charge of will have to supervise a system of several cooperating vehicles such a system were not ready to understand and dialog with performing a joint mission, i.e. a Multi-Agent System this new kind of system, so that the global performance of (MAS) (Johnson 2003; Coppin and Legras 2012). In order the system was potentially spoiled by human intervention.

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.

Ambient activity monitoring systems produce large amounts of data, which can be used for health monitoring.The problem is that patterns in this data reflecting health status are not identified yet. In this paper the possibility is explored of predicting the functional health status (the motor score of AMPS = Assessment of Motor and Process Skills) of a person from data of binary ambient sensors. Data is collected of five independently living elderly people. Based on expert knowledge, features are extracted from the sensor data and several subsets are selected. We use standard linear regression and Gaussian processes for mapping the features to the functional status and predict the status of a test person using a leave-one-person-out cross validation. The results show that Gaussian processes perform better than the linear regression model, and that both models perform better with the basic feature set than with location or transition based features.Some suggestions are provided for better feature extraction and selection for the purpose of health monitoring.These results indicate that automated functional health assessment is possible, but some challenges lie ahead. The most important challenge is eliciting expert knowledge and translating that into quantifiable features.

Statistical models almost always yield predictions that are more accurate than those of human experts. However, humans are better at data acquisition and at recognizing atypical circumstances. We use prediction markets to combine predictions from groups of humans and artificial-intelligence agents and show that they are more robust than those from groups of humans or agents alone.

In this paper we describe the architecture of the OntoGene Relation mining pipeline and some of its recent applications. With this research overview paper we intend to provide a contribution towards the recently started discussion towards standards for information extraction architectures in the biomedical domain. Our approach delivers domain entities mentioned in each input document, as well as candidate relationships, both ranked according to a confidency score computed by the system. This information is presented to the user through an advanced interface aimed at supporting the process of interactive curation.

The humour found in short jokes and their often equivalent newspaper cartoons graphic representations are often de­pendent on the results of ambiguity in human speech. The ambiguities can be unexpected and funny. Sometimes well-known ambiguities cooperatively repeated can also be funny. Captioned cartoons often derive their humour from an unexpected ambiguity that can be understood by a lis­tener who can automatically use world knowledge to re­solve the ambiguity. The question considered here is whether the listener can be a computational device as well as a human and the pragmatic difficulty of applying lin­guistic pragmatics to do so. Computational analysis of nat­ural language statements needs to successfully resolve am­biguous statements. Computerized understanding of dia­logue must not only include syntactic and semantic analy­sis, but also pragmatic analysis. Pragmatics includes an un­der­standing of the speaker’s intentions, the context of the utter­ance, and social implications of human communica­tion, both polite and hostile. Computational techniques can use restricted world knowledge in re­solving ambiguous lan­guage use. This paper considers the prag­matic difficulties in recognizing humour in short jokes as well as their repre­sentation in cartoons.

Humor employs an essential false logic which masks the incongruity of two central meanings that are brought into overlap. Formalizing this false logic—if it exists, exists intersubjectively, and is indeed essential for humor—to a degree that is sufficient for computational detection and generation of humor has been a vexing problem for computational humor research. This paper will outline several such logics, in addition to the default of reasoning in a way that is one degree more implausibly than the most common-sense logic that can connect two meanings. The results are not least influenced by a pilot study asking participants to explain different types of jokes.

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.