'Recall precision' diaporamas de présentation

Lecture 13: Evaluation Cont.

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Performance Evaluation of Information Retrieval Systems

Performance Evaluation of Information Retrieval Systems. Many slides in this section are adapted from Prof. Joydeep Ghosh (UT ECE) who in turn adapted them from Prof. Dik Lee (Univ. of Science and Tech, Hong Kong). Why System Evaluation?.

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CS533 Information Retrieval

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CS533 Information Retrieval

CS533 Information Retrieval. Dr. Michal Cutler Lecture #11 March 1, 1999. This Class. Inference networks conditional independence belief nets Inference nets in Information Retrieval Evaluation. Conditional Independence.

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CS533 Information Retrieval

CS533 Information Retrieval. Dr. Michal Cutler Lecture #11 Feb 29, 2000. This Class. Inference networks conditional independence belief nets Inference nets in Information Retrieval Evaluation. Conditional Independence.

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Lecture 14: Evaluation Cont.

Prof. Ray Larson University of California, Berkeley School of Information Tuesday and Thursday 10:30 am - 12:00 pm Spring 2007 http://courses.ischool.berkeley.edu/i240/s07. Lecture 14: Evaluation Cont. Principles of Information Retrieval. Overview. Review

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Information Retrieval

Information Retrieval. CSE 8337 Spring 2005 Retrieval Evaluation Many slides in this section are adapted from Prof. Raymond J. Mooney in CS378 at UT which were adapted from Prof. Joydeep Ghosh (UT ECE) who in turn adapted them from Prof. Dik Lee (Univ. of Science and Tech, Hong Kong).

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CS533 Information Retrieval

CS533 Information Retrieval. Dr. Michal Cutler Lecture #12 March 3 , 1999. This lecture. Evaluation Creating an inverted index file (sources Managing Gigabytes Witten, Moffat, and Bell, chapter 5. Information retrieval, Grossman and Frieder pages 137-142). Fallout. Fallout=

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Chapter 8

Chapter 8. Evaluating Search Engine. Evaluation. Evaluation is key to building effective and efficient search engines Measurement usually carried out in controlled laboratory experiments Online testing can also be done Effectiveness, efficiency, and cost are related

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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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LIS618 lecture 2

LIS618 lecture 2. Thomas Krichel 2004-02-07. Structure. Theory: information retrieval performance Practice: more advanced dialog. retrieval performance evaluation. "Recall" and "Precision" are two classic measures to measure the performance of information retrieval in a single query.

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Multiple Alignment of Citation Sentences with Conditional Random Fields and Posterior Decoding

Multiple Alignment of Citation Sentences with Conditional Random Fields and Posterior Decoding. Ariel Schwartz, Anna Divoli, and Marti Hearst University of California, Berkeley Supported in part by NSF DBI 0317510. Bioscience literature. Rich, complex and fast growing.

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LIS618 lecture 3

LIS618 lecture 3. Thomas Krichel 2003-02-13. Structure of talk. Document Preprocessing Basic ingredients of query languages Retrieval performance evaluation. document preprocessing. There are some operations that may be done to the documents before indexing lexical analysis

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Search Results

LIS 385T: Information Architecture and Design Search Results By Roger C. Wei 10/22/2002 History of e-Info Searching 1960s Electronically searched info on computer. 1971 End-user search system was introduced. 1985 First commercial CD-ROM appeared.

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Chunking: Shallow Parsing

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Test and Evaluation of An Electronic Database Selection Expert system

Test and Evaluation of An Electronic Database Selection Expert system. Wei Ma (weima@uiuc.edu) Timothy W. Cole (t-cole3@uiuc.edu) University of Illinois at Urbana-Champaign. Overview. Development of the Smart Database Selector Usability Test - Objectives & Methodology Results:

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Precision & Recall

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Evaluating search engines

Evaluating search engines. Sec. 8.6. 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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