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eRulemaking CS501 Presentation 2

eRulemaking CS501 Presentation 2. Who We Are. Sam Phillips MEng in CS Dan Rassi Junior in CS Michael Wang MEng in CS Krzysztof Findeisen Senior in Astro and CS Raymond McGill Senior in IS. Project Overview. Federal Requirement To Read Comments To Proposed Rulemakings

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eRulemaking CS501 Presentation 2

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  1. eRulemakingCS501 Presentation 2

  2. Who We Are • Sam Phillips • MEng in CS • Dan Rassi • Junior in CS • Michael Wang • MEng in CS • Krzysztof Findeisen • Senior in Astro and CS • Raymond McGill • Senior in IS

  3. Project Overview • Federal Requirement To Read Comments To Proposed Rulemakings • Cornell eRulemaking Initiative (CERI) working on a system to Automatically classify comments. • Classification Techniques Need “Supervised Learning”

  4. Overview EARS – Electronic Annotation and Rulemaking System Will provide a single interface for managing comments the government receives as part of its eRulemaking process Will use Natural Language Processing (NLP) tools to automate handling of large comment sets We are working on a prototype EARS for the Legal Information Institute (LII) Tom Bruce of the LII is our chief contact, but we are also working with several other LII groups As of Phase II, we had a simple, nonfunctional website that demonstrated our interface

  5. The Stakeholders Funding: NSF Long-Term Users: Agency Analysts Grantee: Other Universities Grantee: Cornell eRulemaking Initiative Subject Matter Experts: LII Student Annotators Researchers: NLP Group Researchers: Usability Software: Our Group

  6. Term Dictionary Rule / Reg.: Proposed rule by a federal agency Rulemaker / Analyst: Domain expert in agency Issue: A logical facet which the Rule impacts. Annotate / Tag (v): To “highlight” text and associate it with a specific issue. Metadata: Data about Data (e.g. E-mail to/from/size) Tag (n): An issue as metadata Flag (n): Non-issue related metadata (e.g. workflow)

  7. Activities From Start To Phase II • Meetings With Tom Bruce • Introduced Project • Explained Requirements / Known Unknowns • Meetings With LII Student Annotators Heidi Craig and Laura Klimpel • Discussed Current Annotation System • Got Feedback for Early Design Ideas • Created Static Webpage To Prove That It’s Possible • Attended Full CERI Meetings

  8. Example

  9. Activities Since Phase II Report • Creation of Backend / Middleware Architecture • Backend in relational mySQL database • Middleware in OO PHP • Clarification of Some Requirements • XML Format • Color of highlights • Discovery of Some Known Unknowns • How NLP System Should React • How Extra Data Should Be Displayed

  10. System Overview Add / Remove Rules, Tags, Comments Administrator Login Choose Rule Annotator Choose Comment Add / Remove Annotations

  11. Design Overview • Web Site backed by a central database

  12. General Design Strategy • Our system architecture is highly modular • Website, database, etc. can be swapped out easily • All components already available on LII servers

  13. Database Design • Primary goal: flexibility • Unified representation of data • Supports more than our web release will • Lots of room for administrator preferences • Secondary goal: speed • 4000 regulations issued per year • Usually ~100, up to 500,000 comments per regulation • Demands on the LII version will be much lower

  14. Database Design

  15. Database Implementation

  16. Web Technology • Currently using the Drupal Content Management System on LII server to host our web application, however we have minimized this dependence • Website uses JavaScript to dynamically change contents of page when user performs an action • AJAX technology is used to send annotations between client and server without reloading page • Our primary goal has been client compatibility across major browsers and operating systems

  17. Working Demo Go!

  18. Where We’re Going • Documentation • Describe SQL Scheme and ER Diagram To Future CS501 Groups • Include Design Decisions • Include mySQL specific queries • Describe How Implemented Features Work • Low Level (Comments in Code) • High Level (Why Features Are Needed / Trade Offs) • Describe How Unimplemented Features Might Work • Design Considerations • Stakeholders Affected

  19. Where We’re Going (2) • Features • Will Certainly Add • UI To Add / Remove • Comments • Rules • Metadata Sets • Metadata Names • Will Fix UI For • Deleting Comments • Navigating Comments • May Add • Hierarchical Tags • “Fake” NLP Interaction • Multi-user Interaction • NLP XML To/From Connection • Colors

  20. Future Work

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