Kyoto: An Integrated System for Specific Domain WSD

This document describes the preliminary release of the integrated Kyoto system for specific domain WSD. The system uses concept miners (Tybots) to extract domain-related terms and produces a domain-related thesaurus, followed by knowledge-based WSD based on wordnet graphs (UKB). The resulting system can be applied to any language with a lexical knowledge base, and is based on publicly available software and resources. Our participation in Semeval task #17 focused on producing running systems for all languages in the task, and we attained good results in all except Chinese. Due to the pressure of the time-constraints in the competition, the system is still under development, and we expect results to improve in the near future

Emotional Sentence Annotation Helps Predict Fiction Genre

Fiction, a prime form of entertainment, has evolved into multiple genres which one can broadly attribute to different forms of stories. In this paper, we examine the hypothesis that works of fiction can be characterised by the emotions they portray. To investigate this hypothesis, we use the work of fictions in the Project Gutenberg and we attribute basic emotional content to each individual sentence using Ekman’s model. A time-smoothed version of the emotional content for each basic emotion is used to train extremely randomized trees. We show through 10-fold Cross-Validation that the emotional content of each work of fiction can help identify each genre with significantly higher probability than random. We also show that the most important differentiator between genre novels is fear

SentiBench - a benchmark comparison of state-of-the-practice sentiment analysis methods

In the last few years thousands of scientific papers have investigated sentiment analysis, several startups that measure opinions on real data have emerged and a number of innovative products related to this theme have been developed. There are multiple methods for measuring sentiments, including lexical-based and supervised machine learning methods. Despite the vast interest on the theme and wide popularity of some methods, it is unclear which one is better for identifying the polarity (i.e., positive or negative) of a message. Accordingly, there is a strong need to conduct a thorough apple-to-apple comparison of sentiment analysis methods, \textit{as they are used in practice}, across multiple datasets originated from different data sources. Such a comparison is key for understanding the potential limitations, advantages, and disadvantages of popular methods. This article aims at filling this gap by presenting a benchmark comparison of twenty-four popular sentiment analysis methods (which we call the state-of-the-practice methods). Our evaluation is based on a benchmark of eighteen labeled datasets, covering messages posted on social networks, movie and product reviews, as well as opinions and comments in news articles. Our results highlight the extent to which the prediction performance of these methods varies considerably across datasets. Aiming at boosting the development of this research area, we open the methods' codes and datasets used in this article, deploying them in a benchmark system, which provides an open API for accessing and comparing sentence-level sentiment analysis methods

The act of creating humorous acronyms

Our species cannot survive without humor and future human-machine interaction systems will be required to handle humor. From a practical point of view, humor is an important resource for getting selective attention, help in memorizing names and situations, etc. Even if deep modeling of humor in all of its facest is not something available in the near future, there is something concrete that has been achieved and that can help in providing attention to the field. The paper refers to the results of HAHAcronym, a project devoted to humorous acronym production, a circumscribed task that nonetheless requires various generic components. The project opens the way to developments for creative language. Electronic commerce, for instance, will include flexible and individual-oriented humorous promotion more or less as it happens in the world of broadcasted advertisemen

Making Computers Laugh: Investigations in Automatic Humor Recognition

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

Exploring the Lexical Semantics of Dialogue Acts

People proceed in their conversations through a series of dialogue acts to yield some specific communicative intention. In this paper, we study the task of automatic labeling dialogues with the proper dialogue acts, relying on empirical methods and simply exploiting lexical semantics of the utterances. In particular, we present some experiments in both a supervised and an unsupervised framework on an English and an Italian corpus of dialogue transcriptions. In the experiments we consider the settings of dealing with or without additional information from the dialogue structure. The evaluation displays good results, regardless of the used language. We conclude the paper exploring the relation between the communicative goal of an utterance and its affective content

Bringing the Text to Life Automatically

Animated text is an appealing field of creative graphical design. Manually designed text animation is largely employed in advertisement, movie titles and web pages. In this paper we propose to link, through state of the art NLP techniques, the affective content detection of a piece of text to the animation of the words in the text itself. This methodology allows us to automatically generate affective text animation and opens some new perspectives for advertisements, internet applications and intelligent interfaces