The Experts below are selected from a list of 117015 Experts worldwide ranked by ideXlab platform
Yanrui Huang - One of the best experts on this subject based on the ideXlab platform.
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a platform for okapi based Contextual Information retrieval
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2006Co-Authors: Xiangji Huang, Miao Wen, Yanrui HuangAbstract:We present an extensible java-based platform for Contextual retrieval based on the probabilistic Information retrieval model. Modules for dual indexes, relevance feedback with blind or machine learning approaches and query expansion with context are integrated into the Okapi system to deal with the Contextual Information. This platform allows easy extension to include other types of Contextual Information.
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SIGIR - A platform for Okapi-based Contextual Information retrieval
Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '06, 2006Co-Authors: Xiangji Huang, Miao Wen, Yanrui HuangAbstract:We present an extensible java-based platform for Contextual retrieval based on the probabilistic Information retrieval model. Modules for dual indexes, relevance feedback with blind or machine learning approaches and query expansion with context are integrated into the Okapi system to deal with the Contextual Information. This platform allows easy extension to include other types of Contextual Information.
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GrC - Using Contextual Information to improve retrieval performance
2005 IEEE International Conference on Granular Computing, 2005Co-Authors: Xiangji Huang, Yanrui HuangAbstract:In this paper, we propose a Contextual retrieval framework which incorporates the user and global Contextual Information into the probabilistic retrieval model. We investigate different techniques of using Contextual Information to improve Information retrieval performance in details. In particular, (1) we use the related text Contextual Information for query expansion; (2) we use the granularity Information to construct the document level index and paragraph level index; (3) we use the geographic Information for filtering. In addition, a new term weighting function BM5O is proposed based on the global context Information. This framework is adaptable and extensible. If there is a new context category, we can extend the existing search system to accommodate it. Finally, we report our experimental findings on TREC data sets.
Francisco B. Rodriguez - One of the best experts on this subject based on the ideXlab platform.
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Is the Contextual Information relevant in text clustering by compression
Expert Systems with Applications, 2012Co-Authors: Ana Granados, David Camacho, Francisco B. RodriguezAbstract:Usually, when analyzing data that have not been processed or filtered yet, it can be observed that not all the data have equal importance. Thus, it is common to find relevant data surrounded by non relevant one. This occurs when analyzing textual Information due to its intrinsic nature: texts contain words that provide a lot of Information about the subject matter, whereas they contain other words with a little meaning or relevance. We believe that although in principle the non-relevant words are not as important as the relevant ones, the former constitute the substrate that supports the last. Since this substrate is the context that surrounds the relevant Information, we call it the Contextual Information. In this paper, we analyze the relevance that the Contextual Information has in textual data, in a clustering by compression scenario. We generate the Contextual Information applying a distortion technique previously developed by the authors. One of the main characteristics of this technique is that it maintains the Contextual Information. In this paper we compare this technique with three new distortion techniques that destroy the Contextual Information in different ways. The experimental results support our hypothesis that the Contextual Information is relevant at least in the area of text clustering by compression.
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IDEAL - Relevance of Contextual Information in compression-based text clustering
Intelligent Data Engineering and Automated Learning – IDEAL 2010, 2010Co-Authors: Ana Granados, Rafael Martínez, David Camacho, Francisco B. RodriguezAbstract:In this paper we take a step towards understanding compression distances by analyzing the relevance of Contextual Information in compression-based text clustering. In order to do so, two kinds of word removal are explored, one that maintains part of the Contextual Information despite the removal, and one that does not maintain it. We show how removing words in such a way that the Contextual Information is maintained despite the word removal helps the compression-based text clustering and improves its accuracy, while on the contrary, removing words losing that Contextual Information makes the clustering results worse.
Mathew Magimai-doss - One of the best experts on this subject based on the ideXlab platform.
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ICASSP - Exploiting Contextual Information for improved phoneme recognition
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Joel Praveen Pinto, Hynek Hermansky, B. Yegnanarayana, Mathew Magimai-dossAbstract:In this paper, we investigate the significance of Contextual Information in a phoneme recognition system using the hidden Markov model - artificial neural network paradigm. Contextual Information is probed at the feature level as well as at the output of the multilayered perceptron. At the feature level, we analyze and compare different methods to model sub-phonemic classes. To exploit the Contextual Information at the output of the multilayered perceptron, we propose the hierarchical estimation of phoneme posterior probabilities. The best phoneme (excluding silence) recognition accuracy of 73.4% on the TIMIT database is comparable to that of the state-of- the-art systems, but more emphasis is on analysis of the Contextual Information.
Mathew Magimaidoss - One of the best experts on this subject based on the ideXlab platform.
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exploiting Contextual Information for improved phoneme recognition
International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Joel Pravee Pinto, Hynek Hermansky, Mathew MagimaidossAbstract:In this paper, we investigate the significance of Contextual Information in a phoneme recognition system using the hidden Markov model - artificial neural network paradigm. Contextual Information is probed at the feature level as well as at the output of the multilayered perceptron. At the feature level, we analyze and compare different methods to model sub-phonemic classes. To exploit the Contextual Information at the output of the multilayered perceptron, we propose the hierarchical estimation of phoneme posterior probabilities. The best phoneme (excluding silence) recognition accuracy of 73.4% on the TIMIT database is comparable to that of the state-of- the-art systems, but more emphasis is on analysis of the Contextual Information.
Jelena Kovacevic - One of the best experts on this subject based on the ideXlab platform.
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Image Classification with Rejection using Contextual Information
arXiv: Computer Vision and Pattern Recognition, 2015Co-Authors: Filipe Condessa, Jose M. Bioucas-dias, Carlos A. Castro, John A. Ozolek, Jelena KovacevicAbstract:We introduce a new supervised algorithm for image classification with rejection using multiscale Contextual Information. Rejection is desired in image-classification applications that require a robust classifier but not the classification of the entire image. The proposed algorithm combines local and multiscale Contextual Information with rejection, improving the classification performance. As a probabilistic model for classification, we adopt a multinomial logistic regression. The concept of rejection with Contextual Information is implemented by modeling the classification problem as an energy minimization problem over a graph representing local and multiscale similarities of the image. The rejection is introduced through an energy data term associated with the classification risk and the Contextual Information through an energy smoothness term associated with the local and multiscale similarities within the image. We illustrate the proposed method on the classification of images of H&E-stained teratoma tissues.
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ISBI - Classification with reject option using Contextual Information
2013 IEEE 10th International Symposium on Biomedical Imaging, 2013Co-Authors: Filipe Condessa, Jose M. Bioucas-dias, Carlos A. Castro, John A. Ozolek, Jelena KovacevicAbstract:We propose a new algorithm for classification that merges classification with reject option with classification using Contextual Information. A reject option is desired in many image-classification applications requiring a robust classifier and when the need for high classification accuracy surpasses the need to classify the entire image. Moreover, our algorithm improves the classifier performance by including local and nonlocal Contextual Information, at the expense of rejecting a fraction of the samples. As a probabilistic model, we adopt a multinomial logistic regression. We use a discriminative random model for the description of the problem; we introduce reject option into the classification problem through association potential, and Contextual Information through interaction potential. We validate the method on the images of H&E-stained teratoma tissues and show the increase in the classifier performance when rejecting part of the assigned class labels.
