دانلود کتاب Statistical Language Models for Information Retrieval

فهرست مطالب :

Synthesis Lectures on Human Language Technologies
Contents
Preface
Introduction
Basic Concepts in Information Retrieval
Statistical Language Models
Overview of Information Retrieval Models
Similarity-Based Models
Probabilistic Relevance Models
Probabilistic Inference Models
Axiomatic Retrieval Framework
Decision-Theoretic Retrieval Framework
Summary
Simple Query Likelihood Retrieval Model
Basic Idea
Event Models for D
Multinomial D
Multiple Bernoulli D
Multiple Poisson D
Comparison of the Three Models
Estimation of D
A General Smoothing Strategy using Collection Language Model
Jelinek-Mercer Smoothing (Fixed Coefficient Interpolation)
Dirichlet Prior Smoothing
Absolute Discounting Smoothing
Interpolation vs. Backoff
Other Smoothing Methods
Comparison of Different Smoothing Methods
Smoothing and TF-IDF Weighting
Two-Stage Smoothing
Exploit Document Prior
Summary
Complex Query Likelihood Retrieval Model
Document-Specific Smoothing of D
Cluster-Based Smoothing
Document Expansion
Beyond Unigram Models
Parsimonious Language Models
Full Bayesian Query Likelihood
Translation Model
Summary
Probabilistic Distance Retrieval Model
Difficulty in Supporting Feedback with Query Likelihood
Kullback-Leibler Divergence Retrieval Model
Estimation of Query Models
Model-Based Feedback
Markov Chain Query Model Estimation
Relevance Model
Structured Query Models
Negative Relevance Feedback
Summary
Language Models for Special Retrieval Tasks
Cross-Lingual Information Retrieval
Distributed Information Retrieval
Structured Document Retrieval and Combining Representations
Personalized and Context-Sensitive Search
Expert Finding
Passage Retrieval
Subtopic Retrieval
Other Retrieval-Related Tasks
Modeling Redundancy and Novelty
Predicting Query Difficulty
Summary
Language Models for Latent Topic Analysis
Probabilistic Latent Semantic Analysis (PLSA)
Latent Dirichlet Allocation (LDA)
Extensions of PLSA and LDA
Topic Model Labeling
Using Topic Models for Retrieval
Summary
Conclusions
Language Models vs. Traditional Retrieval Models
Summary of Research Progress
Future Directions
Bibliography