• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Monk slides final

Monk slides final



Slides on how to use the text mining tool, MONK.

Slides on how to use the text mining tool, MONK.



Total Views
Views on SlideShare
Embed Views



2 Embeds 325

http://litlibrarian.wordpress.com 245
http://harriettgreen.info 80


Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment

    Monk slides final Monk slides final Presentation Transcript

    • MONK: An Introduction Harriett Green, English and Digital Humanities Librarian“Digital Humanities in Theory and Practice: Tools and Methods for Librarians” June 22, 2012
    • MONK, the project http://monkproject.org MONK, the toolhttps://monk.library.illinois.edu
    • What Is MONK?• A web-based text mining application: https://monk.library.illinois.edu• Contains a closed corpus of curated texts marked up in TEI-A and Part-Of-Speech tagging• Texts selected from public collections (i.e., Documenting the American South) and proprietary (i.e., EEBO)• Analyze texts with tools based on SEASR and Meandre
    • How Do I Use It?• Start at https://monk.library.illinois.edu (Don’t forget to type the encrypted “https”!)
    • Create an Account and Log In• Click on “Begin” button to create an account • If you’re from a CIC institution, click on the “Begin CIC” button in the lower box to log in with your university ID and password• After setting up user name and password, log into MONK 5
    • Available Works
    • Create a Project
    • MONK Workbench“Define Worksets” to build text corpus
    • Create WorksetsSearch for texts to create a workset
    • Create WorksetsSelect texts and click Save to create a Workset
    • View Texts
    • Select Toolsets• Use tools based on SEASR and Meandre (http://seasr.org)• Compare Worksets: Use Dunning’s Log Likelihood or TF/IDF comparisons to analyze two worksets against each other• Classification: Run Naïve Bayes analysis and can also produce Decisions Trees
    • Select Toolsets
    • Run Analysis!
    • You Try It!https://monk.library.illinois.edu
    • MONK in a Nutshell• Read the MONK Tutorials: https://monk.library.illinois.edu/cic/public/tut orial/gettingStarted/gettingStarted.html• A great tool for learning about text mining• For technical help, contact: monkquestions@library.illinois.edu
    • QUESTIONS?Thank you for your time! Harriett Green green19@illinois.edu Twitter: @greenharr