PPT-Frequency and Effects of Search Strategy Characteristics on Relevant Article Retrieval

Whitney A Townsend MLIS Emily C Ginier MLIS Mark P MacEachern MLIS Kate M Saylor MSI Objectives To identify common characteristics of highly effective search strategies for a clinical systematic review topic. 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. Chris Manning, Pandu Nayak and . Prabhakar. . Raghavan. Who are these people?. Stephen Robertson. Keith van . Rijsbergen. Karen . Sp. ä. rck. . Jones. Summary – vector space ranking. Represent the query as a weighted tf-idf vector. 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. CSC . 575. Intelligent Information Retrieval. 2. Source: . Intel. How much information?. Google: . ~100 . PB a . day; 3+ million servers (15 . Exabytes. stored). Wayback Machine has . ~9 . PB + . 100 . . LBSC 708X/INFM 718X. Seminar on E-Discovery. Jason R. Baron. Adjunct Faculty. University of Maryland. March 15, 2012. Overarching Questions. How is the information retrieval task inside organizations like or unlike the problem of finding a good restaurant tonight after class in Baltimore?. Textbook by. Christopher D. Manning, . Prabhakar. . Raghavan. , and . Hinrich. . Schutze. Notes Revised by X. . Meng. for SEU. May 2014. Retrieval Models and Vector Space Model. Retrieval Models. A retrieval model specifies the details of:. The Convergence of IR and IG. Records have a utility. Statutory compliance. Electronic discovery. Operational business value to the organization. Operational Business Value. Classification. Historically obsolete?. Information Retrieval. Information Retrieval. Konsep. . dasar. . dari. IR . adalah. . pengukuran. . kesamaan. sebuah. . perbandingan. . antara. . dua. . dokumen. , . mengukur. . sebearapa. . Christopher . Manning and . Pandu . Nayak. Lecture 14: Learning to Rank. Machine learning for IR ranking?. We’ve looked at methods for ranking documents in IR. Cosine similarity, inverse document frequency, . Hongning. Wang. CS@UVa. What is information retrieval?. CS6501: Information Retrieval. CS@UVa. 2. Why information retrieval . Information overload. “. It refers to the . difficulty. a person can have understanding an issue and making decisions that can be caused by the presence of . 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. All slides ©Addison Wesley, 2008. How Much Data is Created Every . Minute?. Source: . https. ://www.domo.com/blog/2012/06/how-much-data-is-created-every-minute/. The Search Problem. Search and Information Retrieval. Two factors:. A term that appears just once in a document is probably not as significant as a term that appears a number of times in the document.. A term that is common to every document in a collection is not a very good choice for choosing one document over another to address an information need. . Você gosta de emagrecer? Ou de perder 5kg ou 10kg? Independentemente da sua resposta, esse
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