This paper investigates a machine learning approach for temporally ordering and anchoring events in natural language texts. To address data sparseness, we used temporal reasoning ...
Inderjeet Mani, Marc Verhagen, Ben Wellner, Chong ...
Information-extraction (IE) research typically focuses on clean-text inputs. However, an IE engine serving real applications yields many false alarms due to less-well-formed input...
Radu Florian, John F. Pitrelli, Salim Roukos, Imed...
In this paper we present a noun phrase coreference resolution system which aims to enhance the identification of the coreference realized by string matching. For this purpose, we ...
Xiaofeng Yang, Guodong Zhou, Jian Su, Chew Lim Tan
Data sparseness is an ever dominating problem in automatic emotion recognition. Using artificially generated speech for training or adapting models could potentially ease this: t...
Much recent research has been devoted to learning algorithms for deep architectures such as Deep Belief Networks and stacks of auto-encoder variants, with impressive results obtai...
Dumitru Erhan, Yoshua Bengio, Aaron C. Courville, ...