Textual Information Access: Statistical Models

Semi-Supervised and Unsupervised Machine Learning. Data Uncertainty and Important Measures. Adaptive Filtering Prediction and Control. Pursuit of the Universal. Engineering Trustworthy Software Systems. Qualitative Spatial and Temporal Reasoning. Interpolation and Extrapolation Optimal Designs V1. Trends in Parsing Technology. Control Subject to Computational and Communication Constraints.

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• Textual Information Access: Statistical Models by Francois Yvon, Eric Gaussier.
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Textual Information Access: Statistical Models - download pdf or read online

This book presents statistical models that have recently been developed within several research communities to access information contained in text collections. The problems considered are linked to applications aiming at facilitating information access: In order to give the reader as complete a description as possible, the focus is placed on the probability models used in the applications concerned, by highlighting the relationship between models and applications and by illustrating the behavior of each model on real collections.

Textual Information Access is organized around four themes: Classification and Clustering 3. Added to Your Shopping Cart. Description This book presents statistical models that have recently been developed within several research communities to access information contained in text collections.

Probability ranking principle PRP 10 1. Language models 15 1. Informational approaches 21 1. Experimental comparison 27 1.

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Tools for information retrieval 28 1. Bibliography 29 Chapter 2. Application to automatic text summarization 45 2. Application to information retrieval 49 2. Bibliography 54 PART 2: Generalized linear model62 3. Parameter estimation 65 3. Logistic regression 68 3.

Model selection 70 3. Logistic regression applied to text classification 74 3. Bibliography 82 Chapter 4. General principles of kernel methods 88 4. General problems with kernel choices kernel engineering 95 4. Kernel versions of standard algorithms: Kernels for text entities 4.

Bibliography Chapter 5.

Topic-based models 5.