'Query term' presentation slideshows

ADVANCES IN GEOREFERENCED DIGITAL LIBRARIES

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Information Retrieval

Information Retrieval. PengBo Oct 28, 2010. ?????. Introduction of Information Retrieval ???? ? Index Techniques ??? Scoring and Ranking ???? ? Evaluation. Basic Index Techniques. Document Collection. site:pkunews.pku.edu.cn baidu report 12,800 pages Google report 6820 pages.

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Introducing Information Retrieval and Web Search

Introducing Information Retrieval and Web Search. Information Retrieval. Information Retrieval (IR) is finding material (usually documents) of an unstructured nature (usually text) that satisfies an information need from within large collections (usually stored on computers) .

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Document ranking

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Special Topics on Information Retrieval

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Outline

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Outline

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Beyond Bags of Words: A Markov Random Field Model for Information Retrieval

Beyond Bags of Words: A Markov Random Field Model for Information Retrieval. Don Metzler. Bag of Words Representation. 71 the 31 garden 22 and 19 of 19 to 19 house 18 in 18 white 12 trees 12 first 11 a 11 president 8 for 7 as 7 gardens 7 rose 7 tour 6 on 6 was 6 east

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Advisor ? Dr. Hsu Graduate ? Chien-Shing Chen Author ? Pu-Jen Cheng

???????? National Yunlin University of Science and Technology. Translating unknown queries with web corpora for cross-language information retrieval. Advisor ? Dr. Hsu Graduate ? Chien-Shing Chen Author ? Pu-Jen Cheng Jei-Wen Teng

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Boolean Model

Boolean Model. Hongning Wang CS@UVa. Feedback. Evaluation. Abstraction of search engine architecture. Indexed corpus. Crawler. Ranking procedure. Doc Analyzer. (Query). User. Query Rep. Doc Representation. Ranker. Indexer. results. Index. Search with Boolean query.

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CSCI 5417 Information Retrieval Systems Jim Martin

CSCI 5417 Information Retrieval Systems Jim Martin. Lecture 6 9/8/2011. Today 9/8. Review basic Vector Space Model TF*IDF weighting Cosine scoring Ranked retrieval More efficient scoring/retrieval. Summary: tf x idf (or tf.idf). Assign a tf.idf weight to each term i in each document d

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Online Feature Selection for Information Retrieval

Online Feature Selection for Information Retrieval. Niranjan Balasubramanian University of Massachusetts Amherst Joint work with: Giridhar Kumaran and Vitor Carvalho Microsoft Corporation James Allan University of Massachusetts Amherst.

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Score-based ranking of the documents

Score-based ranking of the documents. Submitted By: Kriti Khanna(9910103499) F4, CSE, 4 th year. OUTLINE . Introduction Literature Survey Objective Flowchart Implementation Tools and techniques References. INTRODUCTION. Information Retrieval. Ranking Weight Score.

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Re - ranking R etrieved D ocuments U sing Q uery T erm D istances

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Probabilistic Structured Query Methods

Probabilistic Structured Query Methods. Kareem Darwish, Douglas W.Oard Electrical and Computer Engineering Department, College of Information Studies and UMIACS. Abstract. Structured methods for query term replacement rely on separate estimates of replacement of probabilities of term set.

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The ADEPT Bucket Framework

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Relevance Feedback

Relevance Feedback. Limitations Must yield result within at most 3-4 iterations Users will likely terminate the process sooner User may get irritated at seeing same documents repeated after every iteration It has proven to increase the effectiveness of retrieval.

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