Predicting Candidate Performance From Text Using Deep Learning Models
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This is a talk I gave at PACON. Using text to predict candidate / applicant performance based on historical data. Introduction to natural language processing and deep learning. This can also be used for social media profiling (Facebook), Twitter, Assessment, essay, and resume. Text analytics is much easier than most people thing.
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Predicting Candidate Performance From Text Using Deep Learning Models
- 1. Predicting Candidate Performance From Text (NLP + DBN) Ben Taylor, Chief Data Scientist
- 3. Ben Taylor @bentaylordata Background Personal
- 4. • Sequoia Capital • Largest Video Interviewing Platform • Forbes #10 most promising companies • Global: 189 countries
- 6. GRIT MOTIVATION ENGAGEMENT PERFORMANCE 1 55 80 95% 0 75 10 22% 0 50 20 57% 1 20 90 91% 0 40 60 11% Basic Tutorial On How To Build A Numeric Feature Model BUILDING A MODEL
- 7. ESSAY GRIT MOTIVATION ENGAGEMENT PERFORMANCE I want to work here 1 55 80 95% I have great teamwork 0 75 10 22% Synergy 0 50 20 57% I have so much grit 1 20 90 91% They fired that individual 0 40 60 11% Now what?!? BUILDING A MODEL
- 8. ESSAY PERFORMANCE I want to work here 95% I have great teamwork 22% Synergy 57% I have so much grit 91% They fired that individual 11% Now what?!? BUILDING A MODEL Map: Bad = 0 Good = 1 Better = 2 Best = 3 Tokenize: Female = 1 Male = 1 Female Male 1 0 0 1
- 9. I want to work here have great PERF. 1 1 1 1 1 0 0 95% 1 0 0 0 0 1 1 22% 0 0 0 0 0 0 0 57% 1 0 0 0 0 1 0 91% 0 0 0 0 0 0 0 11% Tokenize the text into unique word columns BUILDING A MODEL ESSAY PERFORMANCE I want to work here 95% I have great teamwork 22% Synergy 57% I have so much grit 91% They fired that individual 11%
- 10. I want to work here have great PERF. 1 1 1 1 1 0 0 95% 1 0 0 0 0 1 1 22% 0 0 0 0 0 0 0 57% 1 0 0 0 0 1 0 91% 0 0 0 0 0 0 0 11% Bag of words modeling, sequence and ordering is lost BUILDING A MODEL
- 11. Bag of words modeling, sequence and ordering is lost BUILDING A MODEL
- 12. I want Want to to go work here PERF. 1 1 1 1 1 95% 1 0 0 0 0 22% 0 0 0 0 0 57% 1 0 0 0 0 91% 0 0 0 0 0 11% Band-Aid: Concept of n-grams BUILDING A MODEL
- 13. RESUME MODELING
- 14. Upload Your Resume Now painstakingly fill out this form containing all of the exact same information
- 15. F 3.95 Data Scientist Yale Sky diving M 2.93 HR Analyst SLCC Poetry F 3.41 Data Munger Harvard Cycling 1 3.95 5 310 56 0 2.93 7 520 91 1 3.41 6 240 56 Name: Sally Taylor GPA: 3.95 Previous Job: Data Scientist School: Yale Hobbies: Sky diving Document modeling review UNSTRUCTURED STRUCTURED MUNGED
- 16. Resume Extension
- 19. GPA Replacement
- 20. Predict Every Point Folds = 9 Fold = 1 Fold = 2… Y_pred
- 21. Mimicking the human recruiter Feature Hunt ONE FEATURE AT A TIME INCREMENTAL GAINS
- 22. DEEP LEARNING
- 23. Unstructured ENGINEERS AND MANUAL FEATURES ARE EXPENSIVE, USING DEEP LEARNING TO AUTOMATE AUTOMATIC FEATURE GENERATION Structured I want Want to to go work here PERF. 1 1 1 1 1 95% 1 0 0 0 0 22% 0 0 0 0 0 57% 1 0 0 0 0 91% 0 0 0 0 0 11% ESSAY I want to work here I have great teamwork Synergy I have so much grit They fired that individual
- 24. ENGINEERS AND MANUAL FEATURES ARE EXPENSIVE, USING DEEP LEARNING TO AUTOMATE AUTOMATIC FEATURE GENERATION ESSAY I want to work here I have great teamwork Synergy I have so much grit They fired that individual ESSAY 3 2 1 4 5 3 7 67 345 54 3 7 99 10234 78 203 501 14 1 2 3 4 5 0 0 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 1 0 0 LSTM RAW TEXT WORD SEQUENCE ENCODING
- 25. BEGIN SCRATCHING AT LAYOUT AUTOMATIC FEATURE GENERATION (LAYOUT) CNN: bit.ly/pacon
- 27. 27 Would you ever hire from just a resume? INTERVIEW MODELING SOFT/TECHNICAL COMPETENCIES Resume can overstate and understate
- 28. Audio VideoText
- 29. QUESTIONS