Includes bibliographical references and index.

Probabilistic models for information retrieval / Stépahne Clinchant and Eric Gaussier -- Learnable ranking models for automatic text summarization and information retrieval / Massih-Réza Amini [and others] -- Logistic regression and text classification / Sujeevan Aseervatham [and others] -- Kernel methods for textual information access / Jean-Michel Renders -- Topic-based generative models for text information access / Jean-Cédric Chappelier -- Conditional random fields for information extraction / Isabelle Tellier and Marc Tommasi -- Statistical methods for machine translation / Alexandre Allauzen and François Yvon -- Information mining: methods and interfaces for accessing complex information / Josiane Mothe, Kurt Englmeier, and Fionn Murtagh -- Opinion detection as a topic classification problem / Juan-Manuel Torres-Moreno [and others] -- Appendix: A. Probabilistic models: an introduction / François Yvon.

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:- information extraction and retrieval;- text classification and clustering;- opinion mining;- comprehension aids (automatic summarization, machine translation, visualization). In order to give the reader as complete a description as possible, the focus is placed on the probability models used in the applications.

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