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 . 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 . INST 734. Doug . Oard. Module 13. Agenda. Image retrieval. Video retrieval. Multimedia retrieval. Multimedia. A set of time-synchronized modalities. Video. Images, object motion, camera motion, scenes. 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. Cristiano Chesi . NETS. , IUSS Center . for . Ne. urocognition and . T. heoretical . S. yntax - Pavia. IGG 40. Università di Trento. Outline. Complexity in Object(-headed) Relative Clauses (ORs). Memory-load accounts. 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. Presentation by:. ABHISHEK KAMAT. ABHISHEK MADHUSUDHAN. SUYAMEENDRA WADKI. 1. Introduction. Mining the data to find interesting patterns, useful insights, customer data and their relationship - data mining . 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. What is IR?. Sit down before fact as a little child, . be prepared to give up every conceived notion, . follow humbly wherever and whatever abysses nature leads, . or you will learn nothing. . . -- Thomas Huxley --. Fatemeh. Azimzadeh. Books. (Manning et al., 2008). Christopher D. Manning, . Prabhakar. . Raghavan. , and . Hinrich. . Schütze. . Introduction to Information Retrieval. Cambridge University Press, 2008. . Diane Litman. Professor, Computer Science Department . Senior Scientist, Learning Research & Development Center . Co-Director, Intelligent Systems Program. University of Pittsburgh. Pittsburgh, . Text 2. Text 3. Text 4. Text 5. Text 6. Text 7. Text 8. Text 9. Text 10. Text 11. Text 12. Text 13. Text 14. Text 15. Text 16. Text 17. Erbauer: . Max Mustermann (Ort). Bauzeit: xx Wochen. Steine: ca. 10.000.