""Into the Wild" ... with Natural Language Processing and Text Classification", Peter Grosskopf, Chief Development Officer at HitFox
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The document outlines a machine learning approach to efficiently screen resumes using natural language processing techniques such as TF-IDF and n-grams for feature extraction. It details the process of preparing data, building a classification model, and evaluating its performance, achieving an AUC of 73.0615%. The conclusion emphasizes the need for further experimentation and development of a continuous learning model for improved results.
""Into the Wild" ... with Natural Language Processing and Text Classification", Peter Grosskopf, Chief Development Officer at HitFox
- 1. …with Natural Language Processing and Text Classification Data Natives 2015 19.11.2015 - Peter Grosskopf
- 2. Hey, I’m Peter. Developer (mostly Ruby), Founder (of Zweitag) Chief Development Officer @ HitFox Group Department „Tech & Development“ (TechDev)
- 3. Company Builder with 500+ employees in AdTech, FinTech and Big Data
- 4. Company Builder = 💡Ideas + 👥People
- 5. How do we select the best people out of more than 1000 applications every month in a consistent way? ? ? ? Machine Learning ?
- 6. Yeah! I found a solution Not really 💩
- 7. Our Goal Add a sort-by- relevance to lower the screening costs and invite people faster
- 8. Let’s Go!
- 9. Action Steps 1. Prepare the textual data 2. Build a model to classify the data 3. Run it! 4. Display and interpret the results
- 10. 1. Prepare Load data Kick out outlier Clean out stopwords (language detection + stemming with NLTK) Define classes for workflow states Link data
- 11. 2. Build a model tf-idf / bag of words !: term-frequency idf: inverse document frequency
- 12. Transform / Quantization from a textual shape to a numerical vector-form I am a nice little text -> v(i, am, a, nice, little, text) -> v(tf*idf, tf*idf, tf*idf, tf*idf, tf*idf, tf*idf)
- 13. term-frequency (tf) Count occurrences in document I am a nice little text -> v(i, am, a, nice, little, text) -> v(1*idf, 1*idf, 1*idf, 1*idf, 1*idf, 1*idf)
- 14. inverse document frequency (idf) Count how often a term occurs in the whole document set and invert with the logarithm d1(I play a fun game) -> v1(i, play, a, fun, game) d2(I am a nice little text) -> v2(i, am, a, nice, little, text) -> v2(1*log(2/2), 1*log(2/1), 1*log(2/2), …) -> v2(0, 0.3, 0, 0.3, 0.3, 0.3)
- 15. bag of words Simple approach to calculate the frequency of relevant terms Ignores contextual information 😢 better: n-grams
- 16. n-grams Generate new tokens by concatenating neighboured tokens example (1 and 2-grams): (nice, little, text) -> (nice, nice_little, little, little_text, text) -> From three tokens we just generated 5 tokens. example2 (1 and 2-grams): (new, york, is, a, nice, city) -> (new, new_york, york, york_is, is, is_a, a, a_nice, nice, nice_city, city)
- 17. vectorize the resumes build 1 to 4 n_grams with Scikit (sklearn) TdIdf-Vectorizer
- 18. Define runtime Train-test-split by date (80/20) Approach: Pick randomly CVs out of the test group Count how many CVs have to be screened to find all the good CVs
- 19. 3. run it! After the resumes are transformed to vector form, the classification gets done with a classical statistical machine learning model (e.g. multinominal-naive-bayes, stochastic-gradient-descent- classifier, logistic-regression and random-forest)
- 20. 4. Results Generated with a combination of stochastic-gradient-descent- classifier and logistic-regression with the python machine-learning library scikit-learn AUC: 73.0615 %
- 21. Wrap Up 1. Prepare 2. Build Model 3. Run 4. Interpret import data vectorize the CVs with 1 to 4 n_grams choose Machine Learning model visualize results clean data define train-test- split run it! Area under curve (AUC)
- 22. Conclusion After trying many different approaches (doc2vec, Recurrent Neuronal Networks, Feature Hashing)- bag of words still the best Explana<on: CV documents do not contain too many semantics
- 23. Outlook Build a better database Experiment with new approaches and tune models Build a continuous learning model