Bayesian learning, widely used in many applied data-modeling problems, is often accomplished with approximation schemes because it requires intractable computation of the posterio...
This paper presents a new approach to improving relation extraction based on minimally supervised learning. By adding some limited closed-world knowledge for confidence estimation...
Feiyu Xu, Hans Uszkoreit, Sebastian Krause, Hong L...
Many algorithms for grammatical inference can be viewed as instances of a more general algorithm which maintains a set of primitive elements, which distributionally define sets of ...
It seems common sense that “policy matters” in setting up e-government interoperability, mainly because collaboration should be guided by dedicated integration objectives and ...
In this paper we present the use of a "general purpose" textual entaiment recognizer in the Answer Validation Exercise (AVE) task. Our system has been developed to learn...