MONK is a digital environment designed to help humanities scholars discover and analyze patterns in the texts they study. The MONK project has been generously supported by the Andrew W. Mellon Foundation, from 2007-2009. All code produced by the project is open source. MONK has a publicly available instance with texts contributed by Indiana University, the University of North Carolina at Chapel Hill, the University of Virginia, and Martin Mueller at Northwestern University:

• The MONK Workbench with publicly available texts (American literature, Shakespeare)

• Software:
• HTML Search/Browse Access to the MONK Datastore
• FeatureLens with Shakespeare or Early American Fiction collections
• TeksTale: Clustering and Word Clouds (log in with user: guest / password: guest)
• Flamenco faceted browsing of MONK Collections
• MONK Project plug-in for Zotero (use with Flamenco to build Zotero collections you can import into MONK as worksets; plug-in ver. 0.1.2 does not work with Zotero ver. 2)
• Downloadable texts, schemas, and source code
• Documentation for:
• Users of The MONK Workbench (see also these training videos on classification and comparison in the MONK Workbench)
• Developers interested in creating components for the MONK Workbench (and a screencast on Using the Spket editor)
• Abbot
• The MONK datastore
• Morphadorner (also available as a PDF file)
• Javadocs for:
• The MONK Datastore
• Morphadorner
• Workbench JSDoc
• Schema documentation for TEI Analytics

An instance of MONK with the publicly available texts plus texts from the Text Creation Partnership (EEBO and ECCO), and texts from ProQuest (Chadwyck Healey's Nineteenth-Century Fiction collection) will be available to scholars at CIC (Big Ten) institutions, once we have integrated MONK with the InCommon authentication framework:

• MONK with restricted-access collection

Information about the project is available here:

• Project Participants
• Background
• HTML snapshot of the MONK project wiki

Recommended Reading:

• Baayen, R. Harald. Analyzing Linguistic Data: A Practical Introduction to Statistics Using R. Cambridge, England: Cambridge University Press, 2008.
• Dunning, T. 1993. "Accurate methods for the statistics of surprise and coincidence." Computational Linguistics 19.1 (Mar. 1993), 61-74.
• Griffiths, D. Head First Statistics. 2009. Second edition. O'Reilly.
• Han J, Kamber M. Data Mining: Concepts and Techniques. Second edition, 2006. Morgan Kaufmann.
• Mosteller, Frederick and Wallace, David L. Applied Bayesian and Classical Inference: The Case of the Federalist Papers. New York: Springer-Verlag, 1984.
• Pei,J, Han J., Mao R. "CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets." Proc. 2000 ACM-SIGMOD Int. Workshop on Data Mining and Knowledge Discovery (DMKD'00), Dallas, TX, May 2000.
• Witten IH, Frank E. Data Mining: Practical Machine Learning Tools and Techniques. Second edition, 2005. Morgan Kaufmann.

If you have questions or comments about MONK, send them to monkproject@lis.illinois.edu. Bugs in MONK software should be reported to monkproject-bugs@lis.illinois.edu.