'Computing recall precision points' diaporamas de présentation

Evaluation of IR Systems

Evaluation of IR Systems. Adapted from Lectures by Prabhakar Raghavan (Yahoo and Stanford) and Christopher Manning (Stanford). This lecture. Results summaries: Making our good results usable to a user How do we know if our results are any good? Evaluating a search engine Benchmarks

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Modern Information Retrieval

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Information Retrieval

Information Retrieval. CSE 8337 (Part D) Spring 2009 Some Material for these slides obtained from: Modern Information Retrieval by Ricardo Baeza -Yates and Berthier Ribeiro-Neto http://www.sims.berkeley.edu/~hearst/irbook/

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Evaluating search engines

Evaluating search engines. Measures for a search engine. How fast does it index Number of documents/hour (Average document size) How fast does it search Latency as a function of index size Expressiveness of query language Ability to express complex information needs

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Information Retrieval and Web Search

Vasile Rus, PhD vrus@memphis.edu www.cs.memphis.edu/~vrus/teaching/ir-websearch/. Information Retrieval and Web Search. Evaluation in Information Retrieval. Outline. There are many retrieval models/ algorithms/ systems, which one is the best? What is the best component for:

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Performance Evaluation

Performance Evaluation. Why System Evaluation?. There are many retrieval models/ algorithms/ systems, which one is the best? What is the best component for: Ranking function (dot-product, cosine, …) Term selection (stopword removal, stemming…) Term weighting (TF, TF-IDF,…)

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Evaluation

Evaluation. Evaluation. The major goal of IR is to search document relevant to a user query. The evaluation of the performance of IR systems relies on the notion of relevance. What constitute relevance ?. Relevance.

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INFORMATION RETRIEVAL TECHNIQUES BY DR . ADNAN ABID

INFORMATION RETRIEVAL TECHNIQUES BY DR . ADNAN ABID. Lecture # 25 BENCHMARKS FOR THE EVALUATION OF IR SYSTEMS. ACKNOWLEDGEMENTS. The presentation of this lecture has been taken from the underline sources

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