'Relevant docs' diaporamas de présentation

2ID10: Information Retrieval Lecture 2: IR Evaluation & Queries

2ID10: Information Retrieval Lecture 2: IR Evaluation & Queries. Lora Aroyo 4 April 2006. User Query. Lecture 1 Summary. Compare the information need with the information. generate a ranking which reflects relevance. Information Need. Ranked list of documents. IR System. feedback.

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Information Retrieval (IR)

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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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Query Operations

Query Operations. Adapted from Lectures by Prabhakar Raghavan (Yahoo, Stanford) and Christopher Manning (Stanford). Recap: Unranked retrieval evaluation: Precision and Recall. Precision : fraction of retrieved docs that are relevant = P(relevant|retrieved)

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Query Operations

Query Operations. Adapted from Lectures by Prabhakar Raghavan (Yahoo, Stanford) and Christopher Manning (Stanford). Recap: Unranked retrieval evaluation: Precision and Recall. Precision : fraction of retrieved docs that are relevant = P(relevant|retrieved)

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Finding Similar Questions in large Question and Answer Archives Jiwoon Jeon , W. Bruce Croft and Joon Ho Lee ACM CIKM

Question Answering from Frequently Asked Question Files Robin D. Burke, Kristian J Hammond, Valdimir Kulyukin , Steven L. Lytinen , Noriko Tomurom and Scott Schoenberg AI magazine; Summer 1997.

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Evaluation

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Day 13

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Evaluation

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Alexander Gelbukh Gelbukh

Special Topics in Computer Science Advanced Topics in Information Retrieval Chapter 3: Goals: Retrieval Evaluation. Alexander Gelbukh www.Gelbukh.com. Previous chapter. Models are needed for formal operations Boolean model is the simplest

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Chapter 4: Advanced IR Models

Chapter 4: Advanced IR Models. 4.1 Probabilistic IR 4.1.1 Principles 4.1.2 Probabilistic IR with Term Independence 4.1.3 Probabilistic IR with 2-Poisson Model (Okapi BM25) 4.1.4 Extensions of Probabilistic IR 4.2 Statistical Language Models 4.3 Latent-Concept Models.

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Information Retrieval

Information Retrieval. scope, basic concepts system architectures, modes of operation. Scope of IR : Getting data relevant to user information needs from: free text (unstructured) recently also: hypertext (WWW) semi-structured (XML)

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Search Engines

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Reference Collections: Task Characteristics

Reference Collections: Task Characteristics. TREC Collection. Text REtrieval Conference (TREC) sponsored by NIST and DARPA (1992-?) Comparing approaches for information retrieval from large text collections: Uniform scoring procedures Large corpus of news and technical texts

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Sabir at TREC 2007 Legal Workshop

Sabir at TREC 2007 Legal Workshop. Chris Buckley Sabir Research cabuckley@sabir.com. Legal Interactive Track. Track Goal: Identify relevant docs for 1 to 12 topics. Score 1 point per relevant document found Score -1/2 point for each non-relevant doc found.

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Also By The Same Author: AKTiveAuthor, A Citation Graph Approach To Name Disambiguation

Also By The Same Author: AKTiveAuthor, A Citation Graph Approach To Name Disambiguation. AKT DTA Colloquium January 23, 2006 Duncan McRae-Spencer. Also By The Same Author. Name ambiguity a problem for automated information extraction. Two problems:

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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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PRES A Score Metric for Evaluating Recall-Oriented IR Applications

SIGIR, 22 July 2010. PRES A Score Metric for Evaluating Recall-Oriented IR Applications. Walid Magdy Gareth Jones Dublin City University. Recall-Oriented IR. Examples: patent search and legal search Objective: find all possible relevant documents Search: takes much longer

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Exercising these ideas

Exercising these ideas. You have a description of each item in a small collection. (30 web sites) Assume we are looking for information about boxers, a type of dog. List the items that are relevant to this information need. (If it is impossible to tell, as with #9, mark it not relevant.)

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