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Utilizing eRecruiter: Enhancing Resume Evaluation with Expert Systems

The eRecruiter project presents an innovative expert system that leverages human expertise to evaluate resumes effectively. By extracting critical facts from resumes and implementing inference rules, it enables quick formation of qualified candidate pools, ranking, and individual resume assessments. This presentation will cover the system's design, including parsing and facts generation techniques using NLTK and BeautifulSoup, as well as the implementation milestones. Join us for a detailed demo and discussion on the workflow, deliverables, and expert collaboration.

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Utilizing eRecruiter: Enhancing Resume Evaluation with Expert Systems

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Presentation Transcript

  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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