By Content Ranking Information Retrieval Software

Description of boolean retrieval, vector space model, probabilistic retrieval, latent semantic indexing and other IR topics. An introduction to various classical ranking methods is also provided.

• Information Retrieval Tutorial - Description of boolean retrieval, vector space model, information retrieval probabilistic ranking retrieval, latent semantic indexing and other information retrieval IR topics. ranking An introduction to various classical information retrieval ranking methods is ranking also provided.
• A Case Study in Web Search Using TREC Algorithms - This study evaluates the performance of a state-of-the-art keyword-based document ranking algorithm (coming out of TREC) on a popular web search task.
• Document Ranking and the Vector-Space Model. - It describes key issues in document ranking techniques based on the vector­ space model. Several TF*IDF variants are discussed. The cosine measure, recall and precision are introduced. [PS format]
• Exploring the Similarity Space - Evaluation of many combinations of term frequency statistics, ranking document frequency by content statistics and document length normalization.[PDF]
• Probabilistic Retrieval - A Chapter in a book which introduces probabilistic by content retrieval.
• Probabilistic Models in Information Retrieval - Introduction to probabilistic models.
• Latent Semantic Indexing: a Probabilistic Analysis - Formal introduction to latent semantic indexing. [PS format]
• Is This Document Relevant? ...Probably - A survey of probabilistic models in information retrieval.[PDF]
• Ranking Algorithms - "Ranking Algorithms" is chapter 14 in the Frakes ranking and Baeza-Yates ranking book. It gives a good discussion ranking of the tradeoffs and ranking choices among different term-weighting ranking strategies.

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