We consider the problem of document conversion from the layout-oriented HTML into a semantic-oriented XML annotation. An important fragment of the conversion problem can be reduced to the sequential learning framework, where source tree leaves are labeled with XML tags. We review sequential learning methods developed for the NLP applications, including the Naive Bayes and Maximum entropy. Then we extend these methods with the hidden markov model (HMM) that injects the transition probabilities into the leaf classification function. Finally, we address the issue of HMM topology. We adopt grammatical inference methods to induce the HMM topology and show how to extend the sequential learning methods accordingly. We test all methods on a particular conversion case and report the evaluation results.
HTML-to-XML Migration by Means of Sequential Learning and Grammatical Inference
| Boris Chidlovskii, Jérôme Fuselier |
| IJCAI 05 Workshop on Grammatical Inference Applications, Edinburgh, Scotland, 30 July, 2005 |
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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