The InfoVis:Wiki project is intended to provide a community platform and forum integrating recent developments and news on all areas and aspects of Information Visualization.
Using editable–by–anyone Wiki technology turned out to be the only way of keeping the presented information up to date and knowledge exchange vivid.
This is the companion website for the following book.
Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, Introduction to Information Retrieval, Cambridge University Press. 2008.
To help researchers investigate relation extraction, we’re releasing a human-judged dataset of two relations about public figures on Wikipedia: nearly 10,000 examples of “place of birth”, and over 40,000 examples of “attended or graduated from an institution”. Each of these was judged by at least 5 raters, and can be used to train or evaluate relation extraction systems. We also plan to release more relations of new types in the coming months.
Short introduction to Vector Space Model (VSM) In information retrieval or text mining, the term frequency - inverse document frequency also called tf-idf, is
The ways of organizing information are finite. It can only be organized by location, alphabet, time, category, or hierarchy. These modes are applicable to almost any endeavor—from your personal file cabinets to multinational corporations. They are the framework upon which annual reports, books, conversations, exhibitions, directories, conventions, and even warehouses are arranged.
This is the project page for SecondString, an open-source Java-based package of approximate string-matching techniques. This code was developed by researchers at Carnegie Mellon University from the Center for Automated Learning and Discovery, the Department of Statistics, and the Center for Computer and Communications Security.
SecondString is intended primarily for researchers in information integration and other scientists. It does or will include a range of string-matching methods from a variety of communities, including statistics, artificial intelligence, information retrieval, and databases. It also includes tools for systematically evaluating performance on test data. It is not designed for use on very large data sets.
J. Lafferty, A. McCallum, and F. Pereira. Proceedings of the Eighteenth International Conference on Machine Learning, page 282--289. San Francisco, CA, USA, Morgan Kaufmann Publishers Inc., (2001)
M. Granitzer, M. Hristakeva, R. Knight, K. Jack, and R. Kern. Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, page 19:1--19:8. New York, NY, USA, ACM, (2012)
Y. Li, A. Wen, Q. Lin, R. Li, and Z. Lu. Web-Age Information Management, volume 6897 of Lecture Notes in Computer Science, Springer, Berlin/Heidelberg, (2011)
X. Chai, B. Vuong, A. Doan, and J. Naughton. Proceedings of the 35th SIGMOD international conference on Management of data, page 87--100. New York, NY, USA, ACM, (2009)
S. Lawrence, K. Bollacker, and C. Giles. Proceedings of the eighth international conference on Information and knowledge management, page 139--146. New York, NY, USA, ACM, (1999)
B. Krause, R. Jäschke, A. Hotho, and G. Stumme. HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia, page 157--166. New York, NY, USA, ACM, (2008)
K. Järvelin, and J. Kekäläinen. SIGIR '00: Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, page 41--48. New York, NY, USA, ACM, (2000)