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 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: informational retrieval and ranking models, classification and clustering (regression logistics, kernel methods, Markov fields, etc.), multilingualism and machine translation, and emerging applications such as information exploration.

Eric Gaussier is deputy director of the Grenoble Informatics Laboratory, one of the largest Computer Science laboratories in France.

François Yvon is professor of Computer Science at the University of Paris Sud in Orsay and member of the Spoken Language Processing group of LIMSI/CNRS, Paris, France.