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. Contents
Part 1: Information Retrieval 1. Probabilistic Models for
Information Retrieval, Stephane Clinchant and Eric Gaussier. 2.
Learnable Ranking Models for Automatic Text Summarization and
Information Retrieval, Massih-Reza Amini, David Buffoni, Patrick
Gallinari,& Tuong Vinh Truong and Nicolas Usunier. Part 2:
Classification and Clustering 3. Logistic Regression and Text
Classification, Sujeevan Aseervatham, Eric Gaussier, Anestis
Antoniadis,& Michel Burlet and Yves Denneulin. 4. Kernel
Methods for Textual Information Access, Jean-Michel Renders. 5.
Topic-Based Generative Models for Text & Information Access,
Jean-Cedric Chappelier. 6. Conditional Random Fields for
Information Extraction, Isabelle Tellier and Marc Tommasi. Part 3:
Multilingualism 7. Statistical Methods for Machine Translation,
Alexandre Allauzen and Francois Yvon. Part 4: Emerging Applications
8. Information Mining: Methods and Interfaces for Accessing Complex
Information, Josiane Mothe, Kurt Englmeier and Fionn Murtagh. 9.
Opinion Detection as a Topic Classification Problem, Juan-Manuel
Torres-Moreno, Marc El-Beze, Patrice Bellot and& Frederic
Bechet.
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