Towards a Benchmark of Natural Language Arguments

The connections among natural language processing and argumentation theory are becoming stronger in the latest years, with a growing amount of works going in this direction, in different scenarios and applying heterogeneous techniques. In this paper, we present two datasets we built to cope with the combination of the Textual Entailment framework and bipolar abstract argumentation. In our approach, such datasets are used to automatically identify through a Textual Entailment system the relations among the arguments (i.e., attack, support), and then the resulting bipolar argumentation graphs are analyzed to compute the accepted arguments

Towards Licenses Compatibility and Composition in the Web of Data

International audienceWe propose a general framework to attach the licensing terms to the data where the compatibility of the licensing terms concerning the data affected by a query is verified, and, if compatible, the licenses are combined into a composite license. The framework returns the composite license as licensing term about the data resulting from the query

Proceedings of the 11th European Agent Systems Summer School Student Session

This volume contains the papers presented at the Student Session of the 11th European Agent Systems Summer School (EASSS) held on 2nd of September 2009 at Educatorio della Providenza, Turin, Italy. The Student Session, organised by students, is designed to encourage student interaction and feedback from the tutors. By providing the students with a conference-like setup, both in the presentation and in the review process, students have the opportunity to prepare their own submission, go through the selection process and present their work to each other and their interests to their fellow students as well as internationally leading experts in the agent field, both from the theoretical and the practical sector. Table of Contents: Andrew Koster, Jordi Sabater Mir and Marco Schorlemmer, Towards an inductive algorithm for learning trust alignment . . . 5; Angel Rolando Medellin, Katie Atkinson and Peter McBurney, A Preliminary Proposal for Model Checking Command Dialogues. . . 12; Declan Mungovan, Enda Howley and Jim Duggan, Norm Convergence in Populations of Dynamically Interacting Agents . . . 19; Akın Günay, Argumentation on Bayesian Networks for Distributed Decision Making . . 25; Michael Burkhardt, Marco Luetzenberger and Nils Masuch, Towards Toolipse 2: Tool Support for the JIAC V Agent Framework . . . 30; Joseph El Gemayel, The Tenacity of Social Actors . . . 33; Cristian Gratie, The Impact of Routing on Traffic Congestion . . . 36; Andrei-Horia Mogos and Monica Cristina Voinescu, A Rule-Based Psychologist Agent for Improving the Performances of a Sportsman . . . 39; --Autonomer Agent,Agent,Künstliche Intelligenz

ACTA: A Tool for Argumentative Clinical Trial Analysis

International audienceArgumentative analysis of textual documents of various nature (e.g., persuasive essays, online discussion blogs, scientific articles) allows to detect the main argumentative components (i.e., premises and claims) present in the text and to predict whether these components are connected to each other by argumentative relations (e.g., support and attack), leading to the identification of (possibly complex) argumentative structures. Given the importance of argument-based decision making in medicine, in this demo paper we introduce ACTA, a tool for automating the argumentative analysis of clinical trials. The tool is designed to support doctors and clinicians in identifying the document(s) of interest about a certain disease, and in analyzing the main argumentative content and PICO elements

Automatic extraction of subcategorization frames for Italian

Subcategorization is a kind of knowledge which can be considered as crucial in several NLP tasks, such as Information Extraction or parsing, but the collection of very large resources including subcategorization representation is difficult and time-consuming. Various experiences show that the automatic extraction can be a practical and reliable solution for acquiring such a kind of knowledge. The aim of this paper is at investigating the relationships between subcategorization frame extraction and the nature of data from which the frames have to be extracted, e.g. how much the task can be influenced by the richness/poorness of the annotation. Therefore, we present some experiments that apply statistical subcategorization extraction methods, known in literature, on an Italian treebank that exploits a rich set of dependency relations that can be annotated at different degrees of specificity. Benefiting of the availability of relation sets that implement different granularity in the representation of relations, we evaluate our results with reference to previous works in a cross-linguistic perspective. 1

Summary Report of The First International Competition on Computational Models of Argumentation

Computational models of argumentation are an active research discipline within Artificial Intelligence that has grown since the beginning of the 1990s (Dung 1995). While still a young field when compared to areas such as SAT solving and Logic Programming, the argumentation community is very active, with a conference series (COMMA, which began in 2006) and a variety of workshops and special issues of journals. Argumentation has also worked its way into a variety of applications. For example, Williams et al. (2015) described how argumentation techniques are used for recommending cancer treatments, while Toniolo et al. (2015) detail how argumentation-based techniques can support critical thinking and collaborative scientific inquiry or intelligence analysis. Many of the problems that argumentation deals with are computationally difficult, and applications utilising argumentation therefore require efficient solvers. To encourage this line of research, we organised the First International Competition on Computational Models of Argumentation (ICCMA), with the intention of assessing and promoting state of the art solvers for abstract argumentation problems, and to identify families of challenging benchmarks for such solvers. The objective of ICCMA’15 is to allow researchers to compare the performance of different solvers systematically on common benchmarks and rules. Moreover, as witnessed by competitions in other AI disciplines such as planning and SAT solving, we see ICCMA as a new pillar of the community which provides information and insights on the current state of the art, and highlights future challenges and developments. This article summarises the first ICCMA held in 2015 (ICCMA’15). In this competition, solvers were invited to address standard decision and enumeration problems of abstract argumentation frameworks (Dunne and Wooldridge 2009). Solvers’ performance is evaluated based on their time taken to provide a correct solution for a problem; incorrect results were discarded. More information about the competition, including complete results and benchmarks, can be found on the ICCMA website

A dataset of RDF licenses

rights and conditions present in licenses for software, data and general works are expressed with the Open Digital Rights Language (ODRL) 2.0 vocabulary and extensions thereof. The dataset contains licenses identified by a dereferenceable URI, which are served with content negotiation providing a double representation for humans and machines alike. This feature enables a generalized machine-to-machine commerce if generally adopted