Broad-coverage grammars tend to be very ambiguous. When such a grammar has to be used on a restricted domain, it can be desirable to specialize it, i.e. to trade part of the coverage for a reduction in ambiguity. This paper presents a novel formulation of grammar specialization as an optimization problem, in which the search is guided by a global measure combining coverage and ambiguity. The method, applicable to any unification grammar with a phrase-structure backbone, is shown to be effective in specializing a broad-coverage LFG for French.
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Corpus-based Grammar Specialization
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| Nicola Cancedda, Christer Samuelsson |
| Proc. of the Fourth Conference on Computational Natural Language Learning (CoNLL'2000), Lisbon, Portugal, 2000 |
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The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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