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E-raamat: Textual Information Access - Statistical Models: Statistical Models [Wiley Online]

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  • Formaat: 448 pages
  • Sari: ISTE
  • Ilmumisaeg: 13-Apr-2012
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1118562798
  • ISBN-13: 9781118562796
Teised raamatud teemal:
  • Wiley Online
  • Hind: 203,00 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
Textual Information Access - Statistical Models: Statistical ModelsSuurem pilt
  • Formaat: 448 pages
  • Sari: ISTE
  • Ilmumisaeg: 13-Apr-2012
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1118562798
  • ISBN-13: 9781118562796
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781118562796
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.
Introduction xiii
Eric Gaussier
Francois Yvon
Part 1 Information Retrieval
1(58)
Chapter 1 Probabilistic Models for Information Retrieval
3(30)
Stephane Clinchant
Eric Gaussier
1.1 Introduction
3(5)
1.1.1 Heuristic retrieval constraints
6(2)
1.2 2-Poisson models
8(2)
1.3 Probability ranking principle (PRP)
10(5)
1.3.1 Reformulation
12(1)
1.3.2 BM25
13(2)
1.4 Language models
15(6)
1.4.1 Smoothing methods
16(3)
1.4.2 The Kullback-Leibler model
19(1)
1.4.3 Noisy channel model
20(1)
1.4.4 Some remarks
20(1)
1.5 Informational approaches
21(6)
1.5.1 DFR models
22(3)
1.5.2 Information-based models
25(2)
1.6 Experimental comparison
27(1)
1.7 Tools for information retrieval
28(1)
1.8 Conclusion
28(1)
1.9 Bibliography
29(4)
Chapter 2 Learnable Ranking Models for Automatic Text Summarization and Information Retrieval
33(26)
Massih-Reza Amini
David Buffoni
Patrick Gallinari
Tuong Vinh Truong
Nicolas Usunier
2.1 Introduction
33(12)
2.1.1 Ranking of instances
34(8)
2.1.2 Ranking of alternatives
42(2)
2.1.3 Relation to existing frameworks
44(1)
2.2 Application to automatic text summarization
45(4)
2.2.1 Presentation of the application
45(3)
2.2.2 Automatic summary and learning
48(1)
2.3 Application to information retrieval
49(5)
2.3.1 Application presentation
49(1)
2.3.2 Search engines and learning
50(3)
2.3.3 Experimental results
53(1)
2.4 Conclusion
54(1)
2.5 Bibliography
54(5)
Part 2 Classification and Clustering
59(162)
Chapter 3 Logistic Regression and Text Classification
61(24)
Sujeevan Aseervatham
Eric Gaussier
Anestis Antoniadis
Michel Burlet
Yves Denneulin
3.1 Introduction
61(1)
3.2 Generalized linear model
62(3)
3.3 Parameter estimation
65(3)
3.4 Logistic regression
68(2)
3.4.1 Multinomial logistic regression
69(1)
3.5 Model selection
70(4)
3.5.1 Ridge regularization
71(1)
3.5.2 LASSO regularization
71(1)
3.5.3 Selected Ridge regularization
72(2)
3.6 Logistic regression applied to text classification
74(7)
3.6.1 Problem statement
74(1)
3.6.2 Data pre-processing
75(1)
3.6.3 Experimental results
76(5)
3.7 Conclusion
81(1)
3.8 Bibliography
82(3)
Chapter 4 Kernel Methods for Textual Information Access
85(44)
Jean-Michel Renders
4.1 Kernel methods: context and intuitions
85(3)
4.2 General principles of kernel methods
88(7)
4.3 General problems with kernel choices (kernel engineering)
95(2)
4.4 Kernel versions of standard algorithms: examples of solvers
97(6)
4.4.1 Kernal logistic regression
98(1)
4.4.2 Support vector machines
99(2)
4.4.3 Principal component analysis
101(1)
4.4.4 Other methods
102(1)
4.5 Kernels for text entities
103(20)
4.5.1 "Bag-of-words" kernels
104(1)
4.5.2 Semantic kernels
105(2)
4.5.3 Diffusion kernels
107(2)
4.5.4 Sequence kernels
109(3)
4.5.5 Tree kernels
112(4)
4.5.6 Graph kernels
116(3)
4.5.7 Kernels derived from generative models
119(4)
4.6 Summary
123(1)
4.7 Bibliography
124(5)
Chapter 5 Topic-Based Generative Models for Text Information Access
129(50)
Jean-Cedric Chappelier
5.1 Introduction
129(6)
5.1.1 Generative versus discriminative models
129(2)
5.1.2 Text models
131(2)
5.1.3 Estimation, prediction and smoothing
133(1)
5.1.4 Terminology and notations
134(1)
5.2 Topic-based models
135(7)
5.2.1 Fundamental principles
135(1)
5.2.2 Illustration
136(2)
5.2.3 General framework
138(1)
5.2.4 Geometric interpretation
139(2)
5.2.5 Application to text categorization
141(1)
5.3 Topic models
142(19)
5.3.1 Probabilistic Latent Semantic Indexing
143(3)
5.3.2 Latent Dirichlet Allocation
146(14)
5.3.3 Conclusion
160(1)
5.4 Term models
161(3)
5.4.1 Limitations of the multinomial
161(1)
5.4.2 Dirichlet compound multinomial
162(1)
5.4.3 DCM-LDA
163(1)
5.5 Similarity measures between documents
164(4)
5.5.1 Language models
165(1)
5.5.2 Similarity between topic distributions
165(1)
5.5.3 Fisher kernels
166(2)
5.6 Conclusion
168(1)
5.7 Appendix: topic model software
169(1)
5.8 Bibliography
170(9)
Chapter 6 Conditional Random Fields for Information Extraction
179(42)
Isabelle Tellier
Marc Tommasi
6.1 Introduction
179(1)
6.2 Information extraction
180(4)
6.2.1 The task
180(2)
6.2.2 Variants
182(1)
6.2.3 Evaluations
182(1)
6.2.4 Approaches not based on machine learning
183(1)
6.3 Machine learning for information extraction
184(3)
6.3.1 Usage and limitations
184(1)
6.3.2 Some applicable machine learning methods
185(1)
6.3.3 Annotating to extract
186(1)
6.4 Introduction to conditional random fields
187(6)
6.4.1 Formalization of a labelling problem
187(1)
6.4.2 Maximum entropy model approach
188(2)
6.4.3 Hidden Markov model approach
190(1)
6.4.4 Graphical models
191(2)
6.5 Conditional random fields
193(10)
6.5.1 Definition
193(2)
6.5.2 Factorization and graphical models
195(1)
6.5.3 Junction tree
196(2)
6.5.4 Inference in CRFs
198(2)
6.5.5 Inference algorithms
200(1)
6.5.6 Training CRFs
201(2)
6.6 Conditional random fields and their applications
203(11)
6.6.1 Linear conditional random fields
204(1)
6.6.2 Links between linear CRFs and hidden Markov models
205(3)
6.6.3 Interests and applications of CRFs
208(2)
6.6.4 Beyond linear CRFs
210(1)
6.6.5 Existing libraries
211(3)
6.7 Conclusion
214(1)
6.8 Bibliography
215(6)
Part 3 MULTILINGUALISM
221(84)
Chapter 7 Statistical Methods for Machine Translation
223(82)
Alexandre Allauzen
Francois Yvon
7.1 Introduction
223(4)
7.1.1 Machine translation in the age of the Internet
223(3)
7.1.2 Organization of the
Chapter
226(1)
7.1.3 Terminological remarks
227(1)
7.2 Probabilistic machine translation: an overview
227(8)
7.2.1 Statistical machine translation: the standard model
228(2)
7.2.2 Word-based models and their limitations
230(4)
7.2.3 Phrase-based models
234(1)
7.3 Phrase-based models
235(15)
7.3.1 Building word alignments
237(8)
7.3.2 Word alignment models: a summary
245(1)
7.3.3 Extracting bisegments
246(4)
7.4 Modeling reorderings
250(9)
7.4.1 The space of possible reorderings
250(5)
7.4.2 Evaluating permutations
255(4)
7.5 Translation: a search problem
259(13)
7.5.1 Combining models
259(2)
7.5.2 The decoding problem
261(1)
7.5.3 Exact search algorithms
262(5)
7.5.4 Heuristic search algorithms
267(5)
7.5.5 Decoding: a solved problem?
272(1)
7.6 Evaluating machine translation
272(7)
7.6.1 Subjective evaluations
273(2)
7.6.2 The BLEU metric
275(2)
7.6.3 Alternatives to BLEU
277(2)
7.6.4 Evaluating machine translation: an open problem
279(1)
7.7 State-of-the-art and recent developments
279(8)
7.7.1 Using source context
279(2)
7.7.2 Hierarchical models
281(2)
7.7.3 Translating with linguistic resources
283(4)
7.8 Useful resources
287(2)
7.8.1 Bibliographic data and online resources
288(1)
7.8.2 Parallel corpora
288(1)
7.8.3 Tools for statistical machine translation
288(1)
7.9 Conclusion
289(2)
7.10 Acknowledgments
291(1)
7.11 Bibliography
291(14)
Part 4 EMERGING APPLICATIONS
305(64)
Chapter 8 Information Mining: Methods and Interfaces for Accessing Complex Information
307(30)
Josiane Mothe
Kurt Englmeier
Fionn Murtagh
8.1 Introduction
307(2)
8.2 The multidimensional visualization of information
309(11)
8.2.1 Accessing information based on the knowledge of the structured domain
309(4)
8.2.2 Visualization of a set of documents via their content
313(4)
8.2.3 OLAP principles applied to document sets
317(3)
8.3 Domain mapping via social networks
320(3)
8.4 Analyzing the variability of searches and data merging
323(4)
8.4.1 Analysis of IR engine results
323(2)
8.4.2 Use of data unification
325(2)
8.5 The seven types of evaluation measures used in IR
327(4)
8.6 Conclusion
331(1)
8.7 Acknowledgments
332(1)
8.8 Bibliography
332(5)
Chapter 9 Opinion Detection as a Topic Classification Problem
337(32)
Juan-Manuel Torres-Moreno
Marc El-Beze
Patrice Bellot
Frederic Bechet
9.1 Introduction
337(2)
9.2 The TREC and TAC evaluation campaigns
339(8)
9.2.1 Opinion detection by question-answering
340(2)
9.2.2 Automatic summarization of opinions
342(1)
9.2.3 The text mining challenge of opinion classification (DEFT (DEfi Fouille de Textes))
343(4)
9.3 Cosine weights - a second glance
347(1)
9.4 Which components for a opinion vectors?
348(4)
9.4.1 How to pass from words to terms?
349(3)
9.5 Experiments
352(5)
9.5.1 Performance, analysis, and visualization of the results on the IMDB corpus
354(3)
9.6 Extracting opinions from speech: automatic analysis of phone polls
357(8)
9.6.1 France Telecom opinion investigation corpus
358(2)
9.6.2 Automatic recognition of spontaneous speech in opinion corpora
360(3)
9.6.3 Evaluation
363(2)
9.7 Conclusion
365(1)
9.8 Bibliography
366(3)
Appendix A Probabilistic Models: An Introduction
369(54)
Francois Yvon
A.1 Introduction
369(1)
A.2 Supervised categorization
370(14)
A.2.1 Filtering documents
370(2)
A.2.2 The Bernoulli model
372(4)
A.2.3 The multinomial model
376(3)
A.2.4 Evaluating categorization systems
379(1)
A.2.5 Extensions
380(3)
A.2.6 A first summary
383(1)
A.3 Unsupervised learning: the multinomial mixture model
384(7)
A.3.1 Mixture models
384(2)
A.3.2 Parameter estimation
386(4)
A.3.3 Applications
390(1)
A.4 Markov models: statistical models for sequences
391(6)
A.4.1 Modeling sequences
391(3)
A.4.2 Estimating a Markov model
394(1)
A.4.3 Language models
395(2)
A.5 Hidden Markov models
397(13)
A.5.1 The model
398(1)
A.5.2 Algorithms for hidden Markov models
399(11)
A.6 Conclusion
410(1)
A.7 A primer of probability theory
411(9)
A.7.1 Probability space, event
411(1)
A.7.2 Conditional independence and probability
412(1)
A.7.3 Random variables, moments
413(5)
A.7.4 Some useful distributions
418(2)
A.8 Bibliography
420(3)
List of Authors 423(2)
Index 425
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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