We present a probabilistic generative model for learning semantic parsers from ambiguous supervision. Our approach learns from natural language sentences paired with world states ...
Label ranking is the task of inferring a total order over a predefined set of labels for each given instance. We present a general framework for batch learning of label ranking f...
We propose a novel HMM-based framework to accurately transliterate unseen named entities. The framework leverages features in letteralignment and letter n-gram pairs learned from ...
Bing Zhao, Nguyen Bach, Ian R. Lane, Stephan Vogel
In this paper, we report on a study that was performed within the "Semantics of History" project on how descriptions of historical events are realized in different types...
Abstract. Traditionally, process mining has been used to extract models from event logs and to check or extend existing models. This has shown to be useful for improving processes ...