BASILE , Valerio

The huge amount of information streaming from online social networking is increasingly attracting the interest of researchers on sentiment analysis on micro-blogging platforms. We provide an overview on the open challenges of sentiment analysis on Italian tweets. We discuss methodological issues as well as new directions for investigation with...

The goal of argument mining is to extract structured information, namely the arguments and their relations, from un-structured text. In this paper, we propose an approach to argument relation prediction based on supervised learning of linguistic and semantic features of the text. We test our method on the CorEA corpus of user comments to online...

Robust, statistical Natural Language Generation from Web knowledge bases is hindered by the lack of text-aligned resources. We aim to fill this gap by presenting a method for extracting knowledge from natural language text, and encode it in a format based on frame semantics and ready to be distributed in the Linked Open Data space. We run an...

Book review

In this paper we present an ongoing work on building a repository of knowledge about objects typically found in homes, their usual locations and usage. We extract an RDF knowledge base by automatically reading text on the Web and applying simple inference rules. The obtained common sense object relations are ready to be used in a domestic...

We present KNEWS, a pipeline of NLP tools that accepts natural language text as input and outputs knowledge in a machine-readable format. The tool outputs frame-based knowledge as RDF triples or XML, including the word-level alignment with the surface form, as well as first-order logical formulae. KNEWS is freely available for download....

International audience

The paper presents an approach to extract knowledge from large text corpora, in particular knowledge that facilitates object manipulation by embodied intelligent systems that need to act in the world. As a first step, our goal is to extract the prototypical location of given objects from text corpora. We approach this task by calculating...

Autonomous robots that are to assist humans in their daily lives are required, among other things, to recognize and understand the meaning of task-related objects. However, given an open-ended set of tasks, the set of everyday objects that robots will encounter during their lifetime is not foreseeable. That is, robots have to learn and extend...

In the last years, emotions recognition tools have become more and more popular, aiming at detecting the emotions of human actors while performing different intelligent tasks by means of headsets and facial emotions detection tools. In addition to this kind of technology , when participants interact with each others by means of tex-tual...

English. The SENTIment POLarity Classification Task 2016 (SENTIPOLC), is a rerun of the shared task on sentiment classification at the message level on Italian tweets proposed for the first time in 2014 for the Evalita evaluation campaign. It includes three subtasks: subjectivity classification , polarity classification, and irony detection. In...

Autonomous robots that are to assist humans in their daily lives must recognize and understand the meaning of objects in their environment. However, the open nature of the world means robots must be able to learn and extend their knowledge about previously unknown objects on-line. In this work we investigate the problem of unknown object...

Intelligent Autonomous Robots deployed in human environments must have understanding of the wide range of possible semantic identities associated with the spaces they inhabit – kitchens, living rooms, bathrooms, offices, garages, etc. We believe robots should learn this information through their own exploration and situated perception in order...