PPT-Information Retrieval and Web Search
Author : phoebe-click | Published Date : 2018-03-20
Link analysis Instructor Rada Mihalcea Note This slide set was adapted from an IR course taught by Prof Chris Manning at Stanford U 2 The Web as a Directed G
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Information Retrieval and Web Search: Transcript
Link analysis Instructor Rada Mihalcea Note This slide set was adapted from an IR course taught by Prof Chris Manning at Stanford U 2 The Web as a Directed G raph Assumption 1 . Miguel Costa. , Daniel Gomes (speaker). Portuguese Web . Archive. Information Retrieval. is the activity of obtaining . information resources relevant. . to an . information need. from a . collection. CSC 575. Intelligent Information Retrieval. Intelligent Information Retrieval. 2. Retrieval Models. Model is an idealization or abstraction of an actual process. in this case, process is matching of documents with queries, i.e., retrieval. Information Retrieval in Practice. All slides ©Addison Wesley, 2008. Search and Information Retrieval. Search on the Web. 1. is a daily activity for many people throughout the world. S. earch and communication are most popular uses of the computer. By . Rong. Yan, Alexander G. and . Rong. Jin. Mwangi. S. . Kariuki. 2008-11629. Quiz. What’s Negative Pseudo-Relevance feedback in multimedia retrieval?. Introduction. As a result of high demand of content based access to video information.. INST 734. Module 3. Doug . Oard. Agenda. Ranked retrieval. Similarity-based ranking. Probability-based ranking. Boolean Retrieval. Strong points. Accurate, . if you know the right strategies. Efficient for the computer. Miguel Costa. , Daniel Gomes (speaker). Portuguese Web . Archive. Information Retrieval. is the activity of obtaining . information resources relevant. . to an . information need. from a . collection. Information. Miles Efron, Jana . Diesner. , Peter . Organisciak. , Garrick Sherman, Ana . Lucic. {. mefron. , et al.}@. illinois.edu. GSLIS 2012. TREC: The Text REtrieval Conference. NIST. Web. Legal. Course summary. Crista. Lopes. Lecture Objective. Know what you know. Problem Space of this course. “Big Data”. How to. collect it. index it. search it for relevant information. Industry segment . ChengXiang. (“Cheng”) . . Zhai. Department of Computer Science. University of Illinois at Urbana-Champaign. http://www.cs.uiuc.edu/homes/czhai. . Email: czhai@illinois.edu. 1. Yahoo!-DAIS Seminar, UIUC. Hongning. Wang. CS@UVa. Classical search engine architecture. “The . Anatomy of a Large-Scale . Hypertextual. Web Search . Engine”. - Sergey . Brin. and . Lawrence Page, . Computer networks and ISDN systems. Hongning. Wang. CS@UVa. Recap: Core IR concepts. Information need. “. an individual or group's desire to locate and obtain information to satisfy a conscious or unconscious need. ” – wiki. An IR system is to satisfy users’ information need. Web Science Course. 2. What to Expect. Information Retrieval Basics. IR Systems. History of IR. Retrieval Models. Vector Space Model. Information Retrieval on the Web. Differences to traditional IR. Selected Papers. Fatemeh. Azimzadeh. Books. (Manning et al., 2008). Christopher D. Manning, . Prabhakar. . Raghavan. , and . Hinrich. . Schütze. . Introduction to Information Retrieval. Cambridge University Press, 2008. . Você gosta de emagrecer? Ou de perder 5kg ou 10kg? Independentemente da sua resposta, esse
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