Data-driven representation learning for words is a technique of central importance in NLP. While indisputably useful as a source of features in downstream tasks, such vectors tend...
In the proposed doctoral work we will design an end-to-end approach for the challenging NLP task of text-level discourse parsing. Instead of depending on mostly hand-engineered sp...
We propose two improvements on lexical association used in embedding learning: factorizing individual dependency relations and using lexicographic knowledge from monolingual dicti...
Social media content can be used as a complementary source to the traditional methods for extracting and studying collective social attributes. This study focuses on the predictio...
Daniel Preotiuc-Pietro, Vasileios Lampos, Nikolaos...
In this paper, we address the problem of evaluating spontaneous speech using a combination of machine learning and crowdsourcing. Machine learning techniques inadequately solve th...