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, Stéphane
Clinchant and Eric Gaussier.
2. Learnable Ranking Models for Automatic Text Summarization and
Information Retrieval, Massih-Réza Amini, David Buffoni,
Patrick Gallinari, & #8232; Tuong Vinh Truong and Nicolas
Usunier.
Part 2: Classification and Clustering
3. Logistic Regression and Text Classification, Sujeevan
Aseervatham, Eric Gaussier, Anestis Antoniadis, & #8232; Michel
Burlet and Yves Denneulin.
4. Kernel Methods for Textual Information Access, Jean-Michel
Renders.
5. Topic-Based Generative Models for Text & #8232;Information
Access, Jean-Cédric 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 François 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-Bèze, Patrice Bellot and& #8232;
Fréderic Béchet.
Giới thiệu về tác giả
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.
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