Motivation:Looking for relevant publications for manual database annotation is a tedious task. In this paper, we show that the combination of natural language processing (NLP) qnd clqssificqtion tools cqn help re-ranking the documents returned by PubMed according to their relevance to SWISS-PROT annotation. Results:With q probabilistic latent categoriser (PLC)we obtained 69% recall and 59% precision for relevant documents in representative query. As the PLC technique provides the relative contribution of each term to the final document score, we used the Kullback-Leibler symmetric divergence to determine the most discriminating words for SWISS-PROT medical annotation. This information should allow curators to better apprehend classification results and has also a great value for fine-tuning the linguistic pre-processing of documents, which in turn can improve the overall classifier performance.
Combining NLP and Probabilistic Categorisation for Document and Term Selection for SWISS-PROT Medical Annotation
| Pavel Dobrokhotov, Cyril Goutte, Anne-Lise Veuthey, Eric Gaussier |
| Proceedings of the 11th International Conference on Intelligent Systems for Molecular Biology (ISMB 2003) |
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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