PPT-Information Retrieval Text Mining - Text Mining & Information Retrieval

Information Retrieval Information Retrieval Konsep dasar dari IR adalah pengukuran kesamaan sebuah perbandingan antara dua dokumen mengukur sebearapa . 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 . 1. Overview . This presentation is for chapter 16 which discuss :. Chapter . 16: Text Mining for Translational . Bioinformatics. 1- terminologies.. 2- definitions.. 2-uses cases and applications.. 3-evaluation techniques and evaluation metrics.. Opportunities and Barriers. John . McNaught. Deputy Director. National Centre for Text Mining. John.McNaught@manchester.ac.uk. Topics. What is text mining? (briefly). What can it offer? (selectively). . lecture 2. history. Thomas . Krichel. 2011-09-19 . contents . based on a very fine paper by Michael Lesk “The Seven Ages of Information Retrieval”. . That paper was written in 1997, so it does not cover recent advances.. 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. Manuel Montes, Hugo . Jair. Escalante. Instituto Nacional de Astrofísica, Óptica y Electrónica, México.. http://ccc.inaoep.mx/~mmontesg. /. http://ccc.inaoep.mx/~hugojair/. {. mmontesg. , . hugojair. 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. CSC 575. Intelligent Information Retrieval. Intelligent Information Retrieval. 2. Web Mining. Today. Overview of Web Data Mining. Web Content Mining / Text Mining. Web Usage Mining. Web Personalization. What Is . T. ext . M. ining?. Also known as . Text Data Mining. Process of . examining large collections of . unstructured. textual . resources in order to generate new information, typically using specialized computer software. 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. Hongning. Wang. CS@UVa. CS@UVa. CS6501: Information Retrieval. 1. Abstraction of search engine architecture. User. Ranker. Indexer. Doc Analyzer. Index. results. Crawler. Doc . Representation . Query Rep. Fatemeh. Azimzadeh. Books. (Manning et al., 2008). Christopher D. Manning, . Prabhakar. . Raghavan. , and . Hinrich. . Schütze. . Introduction to Information Retrieval. Cambridge University Press, 2008. . Universität des Saarlandes, Saarbrücken. Winter Semester 2011/12. The Course. Lecturers. Teaching Assistants. October 18, 2011. I.. 2. IR&DM, WS'11/12. D5: Databases & Information Systems Group . Cal Poly Pomona. Today. Who I am. CS 599 educational objectives (and why). Overview of the course, and logistics. Quick overview of IR and why we study it. 2. Who am I?. Instructor : . Sampath . Jayarathna.