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eRecruiter Expert System

eRecruiter Expert System. Presenters: Date. 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

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eRecruiter Expert System

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  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 1 Facts generation 3 Explanation 2 Run CLIPS

  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 3-1Resume 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. HTMLresume to CLIPS facts Text area and attributes of objects DOM objects DOM root

  15. HTML structure …… <div id="company1" title="ClearNet Security"> <div id="position11">Consultant</div> <div id="exp_time11">January 2010-April 2010</div> <div id="experience11">Worked on Ruby on Rails application creating matching algorithms and UPC database.</div> </div> ……

  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 slotto uniquely identify a candidate.

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