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. Lin, R. Nogueira, and A. Yates. (2020)cite arxiv:2010.06467Comment: Final preproduction version of volume in Synthesis Lectures on Human Language Technologies by Morgan & Claypool.
S. Hachmeier, R. Jäschke, and H. Saadatdoorabi. Proceedings of the Conference on ``Lernen, Wissen, Daten, Analysen'', 3341, page 213--226. Aachen, (2022)
G. Feng, T. Liu, Y. Wang, Y. Bao, Z. Ma, X. Zhang, and W. Ma. Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR \textquotesingle06, ACM Press, (2006)
B. Powley, and R. Dale. Large Scale Semantic Access to Content (Text, Image, Video, and Sound), page 618--632. Paris, France, France, LE CENTRE DE HAUTES ETUDES INTERNATIONALES D'INFORMATIQUE DOCUMENTAIRE, (2007)