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Despite the rich information provided by sensors such as the Microsoft Kinect in the robotic perception setting, the problem of detecting object instances remains unsolved, even in the tabletop setting, where segmentation is greatly simplified. Existing object detection systems often focus on textured objects, for which local feature descriptors can be used(More)
We propose an approach to extracting information from text based on the hypothesis that text sometimes describes the world. The hypothesis is embodied in a generative probability model that describes (1) possible worlds and the facts they might contain, (2) how an author chooses facts to express, and (3) how those facts are expressed in text. Given text,(More)
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