AI*IA 2014 Tutorials

Assume a market research agency wants to find out how many among the tweets mentioning product X express a favourable view of it. On the surface, this seems a standard instance of query-biased tweet sentiment classification. However, the agency is likely not interested in whether a specific individual has a positive view of X, but in knowing #how many# among the people who tweet about X have a positive view of it; that is, the agency is actually interested in knowing the relative frequency of the positive class, i.e., is interested not in classification but in quantification. Essentially, quantification is classification evaluated at the aggregate (rather than at the individual) level.

The research community has recently shown a growing interest in tackling quantification as a task in its own right. One of the reasons is that, since the goal of quantification is different than that of classification, quantification requires evaluation measures different from those used for classification. A second, related reason is that, as it has been shown, using a method optimized for classification accuracy is suboptimal when quantification accuracy is the real goal. A third reason is the growing awareness that quantification is going to be more and more important; with the advent of big data, more and more application contexts are going to spring up in which we will simply be happy with analyzing data at the aggregate level rather than at the individual level.

The goal of this tutorial is to introduce the audience to the problem of quantification, to the techniques that have been proposed for solving it, to the metrics used to evaluate them, and to the problems that are still open in the area. Applications in fields as diverse as machine learning, natural language processing, data mining, epidemiology, social sciences, and still others, will be discussed.

Home page: Fabrizio Sebastiani@ISTI-CNR

Many real-world scenarios involve hybrid systems, which are described by both continuous control variables and discrete logical modes. Some example applications include autonomous underwater vehicles, smart power management, autonomous drones, satellite observation planning. Such scenarios motivate the need to reason with mixed discrete-continuous domains from a verification as well as a planning perspective.

In this tutorial, I will provide an introduction to AI Planning, with a particular focus on modelling planning problems in hybrid domains. I will consider some working examples based on real-world scenarios, showing how even complex interactions can be easily modelled using the planning formalism. In the second part of the tutorial, I will present some tools for solving planning problems and discuss what challenges arise when one wants to use plans in practice: plan validation, plan execution and replanning, planning-control-robotics integration. The tutorial will end with a discussion on current challenges and open problems.

The target is the broad audience of AI*IA, as the tutorial covers multidisciplinary topics, relevant to planning, machine learning, robotics, verification etc., with a proper mix of theory and applications. No prior knowledge of planning is required to follow the tutorial.

Home page: Daniele Magazzeni@King's College

In this tutorial, I will showcase for the first time BabelNet 3.0 (http://babelnet.org), a new release of the largest multilingual encyclopedic dictionary and semantic network, which now covers around 260 languages and provides both lexicographic and encyclopedic knowledge for all the open-class parts of speech (nouns, verbs, adjectives and adverbs), thanks to the seamless integration of WordNet, Wikipedia, Wiktionary, OmegaWiki, Wikidata and the Open Multilingual WordNet. BabelNet 3.0 features a full-fledged taxonomy both for concepts (car is-a motor vehicle) and named entities (Donald Duck is-a character), thanks to the integration of the Wikipedia Bitaxonomy (http://wibitaxonomy.org).

In the second part of the tutorial, I will present Babelfy (http://babelfy.org), a unified approach that leverages BabelNet to perform word sense disambiguation and entity linking in arbitrary languages, with performance on both tasks on a par with, or surpassing, those of task-specific state-of-the-art supervised and knowledge-based systems. We will conclude with ideas and future directions on this beautiful multilingual world.

Both parts of the tutorial will feature hands-on sessions for Java developers and SPARQL fans. The tutorial does not require any specific knowledge of Natural Language Processing and targets anyone interested in multilinguality, lexical knowledge representation, language ambiguity and disambiguation.

Home page: Roberto Navigli@Sapienza