1. eRecruiter Expert System Presenters: Date

2. Agenda Review (Wei 2 mins) – Problem domain – Overview of the system Milestones (Jon S. 2 mins) – Timeboxes – Deliverables Meetings with experts (Max or/and Jon M. 2 mins) – With Steve Saunder Nuts and Bolts (all 8 mins) – Work division – Implementation of each part of the system Demo and discussion (Jon S. 6 mins)

3. Introduction and Overview

4. eRecruiter Problem domain: – eRecruiter is an expert system that help judge a resume according to the knowledge extracted from a human expert. As an expert system: – Facts from resumes. – Templates to define the structure of facts and knowledge. – Inference rules for scoring and weighting facts and making decisions. – Explanation for explaining the results of judgments. Use cases of the system: – Quickly create a pool of qualified resumes. – Rank resumes. – Judge an individual resume.

5. System design: components Facts generation Run CLIPS Explanation 1 2 3

6. Step 3-1 Generate facts wxPython and Python Beautifulsoup, NLTK and Python

7. Step 3-2 Run CLIPS Python and PyCLIPS

8. Step 3-3 Explanation Python and wxPython

9. Milestones Jon S. part goes from here

10. Meetings with experts Max and Jon M. part goes here

11. Work divisions (pls edit based on your needs:)) Individual accomplishment: – Max and Jon M: – Jon S.: – Wei: resume formatting, resume parsing, resume CLIPS facts generation. Shared accomplishments: – Discussion on the overall design of the system. – Preparation of knowledge base. – Discussion on facts structure and inference rules. – Discussion on scoring strategy and explanation system. – Timebox, deliverables, expert contact and group meetings.

12. Bolts and Nuts Part 3-1 Resume parsing and facts generation

13. NLTK and Beautifulsoup NLTK (natural language toolkit) is used to extract resume facts based on linguistic patterns. – “(I) Worked on Ruby on Rails application creating matching algorithms and UPC database.” – I/PRP worked/VBD on/IN Ruby/NNP on/IN Rails/JJ application/NN creating/VBG matching/VBG algorithms/NNS and/CC UPC/NN database/NN./. Beautifulsoup, a python library handling DOM objects.

14. HTML resume to CLIPS facts HTML resume Experience Position Leadership quality Experience description Work area quality Duration Loyalty qualitySkills Skill qualities Certifications Certification qualities Education Degree Degree quality School School rank quality Major Major quality DOM root DOM objects Text area and attributes of objects

15. HTML structure …… Consultant January 2010-April 2010 Worked on Ruby on Rails application creating matching algorithms and UPC database. ……

16. Deftemplates for these facts are predefined.

17. Coding convention Resume facts CLIPS file is named uniquely as ID_Name.clp. Each deffacts has a ID slot to uniquely identify a candidate.