The DebateLab project is conducting research towards representing, mining and reasoning with online arguments. The goal of DebateLab is to offer a suite of tools and services that will assist both the work of the professional journalist in accomplishing everyday tasks (e.g., writing, archiving, retrieving articles), as well as the activity of the ordinary Web user (reader) who wishes to be well-informed about topics or entities of interest (e.g., persons, locations, events).

DebateLab is conducted at FORTH-ICS in collaboration with SKEL-NCSR “Demokritos”, by a balanced consortium comprising senior, principal and postdoctoral researchers, PhD students and a software engineer.

For more information, visit the FORTH website here.

This poster presents the work that has be done in the context of the DebateLab project, by briefly describing the pipeline of the EL-NEL implementation for non-English language, the modular architecture and alternative options for each component of EL-NEL. The aim of this work is to present the process followed for identifying sub-strings (entity-mentions) within an unstructured textual argument and then mapping them to uiniquely identified entity descriptions provided in the Knowledge Bases (KBs).

For more information, see the paper here.

A demo is also available, here.

This work considers the collection of deceptive April Fools’ Day (AFD)
news articles as a useful addition in existing datasets for deception detection tasks. Such collections have an established ground truth and are relatively easy to construct across languages. As a result, we introduce a corpus that includes diachronic AFD and normal articles from Greek newspapers and news websites. On top of that, we build a rich linguistic feature set, and analyze and compare its deception cues with the only AFD collection currently available, which is in English. Following a current research thread, we also discuss the individualism/collctivism dimension in deception with respect to these two datasets. We also build classifiers by testing various monolingual and crosslingual settings. The results showcase that AFD datasets can be helpful in deception detection studies, and are in alignment with the observations of other deception detection works.

For more information, see the paper here.

This work describes DebateLab, a project which aims to conduct research towards developing the theoretical infrastructure for mining, representing and reasoning with arguments found online, while delivering a suite of tools and services supporting the uptake of the related technologies. DebateLab will pave the way for a new Web paradigm, where the different types of arguments and human deliberation will be amenable to algorithmic processing and machine interpretable representation. Towards this, DebateLab analyzes articles with argumentative content to provide tools that will be useful for the professional journalist, but also for users who want to be better informed regarding public debates.

For more information, see the paper here.