We study self-training with products of latent variable grammars in this paper. We show that increasing the quality of the automatically parsed data used for self-training gives h...
This paper proposes a method for extracting high-level rules for expository dialogue generation. The rules are extracted from dialogues that have been authored by expert dialogue ...
This paper shows how the best data-driven dependency parsers available today [1] can be improved by learning from unlabeled data. We focus on German and Swedish and show that label...
In this second part of our state-of-the-art overview on aggregation theory, based again on our recent monograph on aggregation functions, we focus on several construction methods ...
Michel Grabisch, Jean-Luc Marichal, Radko Mesiar, ...
Learning for sentence re-writing is a fundamental task in natural language processing and information retrieval. In this paper, we propose a new class of kernel functions, referre...