A Feature-Centric View of Information Retrieval

Commercial Web search engines such as Google, Yahoo, and Bing are used every day by millions of people across the globe. With their ever-growing refinement and usage, it has become increasingly difficult for academic researchers to keep up with the collection sizes and other critical research issues related to Web search, which has created a divide between the information retrieval research being done within academia and industry. Such large collections pose a new set of challenges for information retrieval researchers. In this work, Metzler describes highly effective information retrieval mod.

5.2 TREC Evaluation.

A Feature-Centric View of Information Retrieval; Acknowledgements; Contents; Chapter 1: Introduction; 1 From Archie to Google and Beyond; 2 The Academic and Industrial Perspectives; 3 Paradigm Shifts; 4 A Robust Retrieval Model; 5 Outline; Chapter 2: Classical Retrieval Models; 1 Overview; 2 Bag of Words Models; 2.1 Binary Independence Retrieval Model; 2.1.1 Estimation with Relevance Information; 2.1.2 Estimation Without Relevance Information; 2.1.3 Tree Dependence Model; 2.2 2-Poisson Model; 2.3 BM25 Model; 2.4 Unigram Language Models; 2.5 Other Bag of Words Models.

3 Models That Go Beyond the Bag of Words Assumption3.1 n-Gram Language Models; 3.2 Indri Inference Network Model; 3.2.1 Document Representation; 3.2.2 Language Models; 3.2.3 Representation Nodes; 3.2.4 Query Nodes; 3.2.5 Explicit vs. Implicit Query Generation; 3.3 Other Models That Go Beyond the Bag of Words Assumption; 4 The Current State-of-the-Art; Chapter 3: Feature-Based Ranking; 1 Overview; 2 Modeling Relevance; 3 The Markov Random Field Model; 3.1 Graph Structure; 3.2 Potential Functions; 4 Constructing Markov Random Fields; 4.1 Dependence Model Type; 4.2 Clique Set Type.

4.3 Weighting Function4.3.1 Weighting…