collections. The problems considered are linkedto 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 theapplications concerned, by highlighting the relationship betweenmodels and applications and by illustrating the behavior of eachmodel on real collections.Textual Information Access is organized around four themes:informational retrieval and ranking models, classification andclustering (regression logistics, kernel methods, Markov fields,etc.), multilingualism and machine translation, and emergingapplications such as information exploration.ContentsPart 1: Information Retrieval1. Probabilistic Models for Information Retrieval, StéphaneClinchant and Eric Gaussier.2. Learnable Ranking Models for Automatic Text Summarization andInformation Retrieval, Massih-Réza Amini, David Buffoni,Patrick Gallinari,
 Tuong Vinh Truong and NicolasUsunier.Part 2: Classification and Clustering3. Logistic Regression and Text Classification, SujeevanAseervatham, Eric Gaussier, Anestis Antoniadis,
 MichelBurlet and Yves Denneulin.4. Kernel Methods for Textual Information Access, Jean-MichelRenders.5. Topic-Based Generative Models for Text 
InformationAccess, Jean-Cédric Chappelier.6. Conditional Random Fields for Information Extraction, IsabelleTellier and Marc Tommasi.Part 3: Multilingualism7. Statistical Methods for Machine Translation, Alexandre Allauzenand François Yvon.Part 4: Emerging Applications8. Information Mining: Methods and Interfaces for Accessing ComplexInformation, Josiane Mothe, Kurt Englmeier and Fionn Murtagh.9. Opinion Detection as a Topic Classification Problem, Juan-ManuelTorres-Moreno, Marc El-Bèze, Patrice Bellot and
Fréderic Béchet.

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