We address the problem of using partially labelled data, eg large collection were only few data is annotated, for extracting entities. Our approach relies on a combination of probabilistic models, which we use to model the generation of entitties and their context, and kernel machines, which implement powerful categorisers based on a similarity measure and some labelled data. This combination takes the form of the so-called Fisher Kernels which implement a similarity based on an underlying probabilistic model. Such kernels are compared with transductive inference, an alternative approach to combining labelled and unlabelled data, again coupled with Support Vector Machines. Experiments are performed on a database of abstracts extracted from Medline.
Combining Labelled and Unlabelled Data : a Case Study on Fisher Kernels and Transductive Inference for Biological Entity Recognition
| Cyril Goutte, Hervé Déjean, Eric Gaussier, Jean-Michel Renders, Nicola Cancedda |
| Proc. of Sixth Conference on Natural Language Learning (CoNLL-2002), Taipei, Taiwan, 24-25 August, 2002. |
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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