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
Collect current data from 22,000 water monitoring points in 27 countries and 1,000 air monitoring stations in 32 countries, and integrate it with historical data collected over the years
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)
J. Teevan, C. Alvarado, M. Ackerman, and D. Karger. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, page 415--422. New York, NY, USA, ACM, (2004)
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)