Book description
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, 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-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 Fréderic Béchet.
