![Information extraction flow
links screenshot
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code extraction
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search index
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Async i/o & search index creation. Indexes (candidate vectors) generated/stored on-the-fly.](https://image.slidesharecdn.com/deeplearningopening-170623095055/85/Machine-intelligence-in-HR-technology-resume-analysis-at-scale-Adrian-Mihai-4-320.jpg)
Machine intelligence in HR technology: resume analysis at scale - Adrian Mihai
- 1. Machine intelligence in HR technology: resume analysis at scale. Similarity matching, resume processing and no-frills deep learning models deployment
- 2. Matching jobs to people — We apply data science over large numbers of resumes in real time telling recruiters who the most qualified candidates are for their job requirements and explain why. Resumes processing and profile analysis — Opening scans through resume files and database candidate profiles to recommend the perfect candidates for any given raw job description by analyzing patterns in candidate history, weighing up skills and fetching candidate code & portfolios to support the decision. A high level overview of our platform is here: https://speakerdeck.com/amorroxic/opening-dot-io-system-architecture
- 3. Quick overview: resume logic pipeline input doc->pdf string byte array (pdf) read pdf resume text download byte array feature extraction topics extraction json json education parser json json … (10 other tasks) elasticsearch percolator combine json json text stream extra tasks json regex (email, etc) salary regression json … Reactive streams - successive aggregation of state generated by specialized actors
- 4. Information extraction flow links screenshot array[url] screenshot screenshot … json json json code extraction json combine json json links/emails/phones/etc github link simeria http call combine jsontext regex … search index experience vector summary vector json json vec vec Async i/o & search index creation. Indexes (candidate vectors) generated/stored on-the-fly.
- 5. Matching pipeline provided title search job description job title neural parsing encoder network neural parsing dense vector encoder network dense vector Matching jobs/candidates and people similar to each other in high volumes of resumes — All input encoded as dense vectors Similarity = angular/cosine sim between sets of encodings
- 6. Real time queries random projection trees candidates candidates A * x + B * (1-x) random projection trees Fast matching - computing similarity over vast vector collections (x2) — Expensive to compute similarity metrics in real time -> k-nn approximations. dense input dense input job title x - search biasjob description
- 7. PARSING: Multi-class, seq2seq, character-level output (dates / OOV names / ..) SIMILARITY/ENCODERS: siamese networks UP-SKILLING model ensembles (input -> latent space -> salary regression -> sequences) SUMMARIES current area of research We train multiple models for various contexts (jobs / resumes / ..) Encoding input and NLP models architecture General considerations — Mostly seq2seq, siamese, attention architectures Input is mostly word vectors - however at times we augment input features with ngrams / character-level information
- 8. Caution on word embedding Potentially trivial example, however - ideal to have models trained on data specific to a particular problem domain — fastText (own corpus, 10gb) “scala’s”, “java/c++/scala”, “java/scala”, “clojure”, .. similarity “scala” - “opera” = 0.17 (very syntax oriented) fastText (own corpus, no character n-grams) “kotlin”, “clojure”, “haskell”, “scala’s”, “f#”, .. similarity “scala” - “opera” = 0.14 (good) fastText (facebook pre-trained vectors, en wiki) “traviata”, “barbiere”, “teatro”, “verdi”, .. similarity “scala” - “opera” = 0.57 (very broad) word2vec (own corpus) “kotlin”, “clojure”, “haskell”, “f#”, .. similarity “scala” - “opera” = 0.05 (very specific) syntactic bias char n-grams in skipgram/cbow semantic biasno char n-grams in skipgram/cbow“scala”
- 9. Similarity network architecture Sequence encoders and similarity core — Recurrent networks sharing weights (siamese architecture) x 1 (b) x 2 (b) x 3 (b) machine learning rocks h 1 (b) h 2 (b) h 3 (b) x 1 (a) x 2 (a) x 3 (a) x 4 (a) she loves data science h 1 (a) h 2 (a) h 3 (a) h 4 (a) objective score Input encoding derives from the trained sim network: activations from the last dense layer before output.
- 10. Models as http micro-services — Components: Simeria (horizontal scale), Yenisei (vertical), model servers All native binaries - golang (simeria), c (yenisei & model servers) Identical provisioning for dev/prod (Ansible) and model hot-swap / roll-back with 0 downtime (Tensorflow serving), AWS/Azure VMs. Deployments at scale - opening Baikal vm’s json processing / search simeria … vector candidates yenisei model server model server model server yenisei model server model server model server horizontal verticalvertical http http, grpc grpc LSH query
- 11. Search approximation take 1: random projection trees Forced to optimize this from day one: not a problem of high traffic on regular usage, instead one of large spikes in I/O at ingestion, each customer having potentially 1M+ resumes = 60M i/o requests (conversions/screenshots/etc), 100m queries (regressions, vectors, etc) and real-time search. — Reduced number of lookups via hyperplanes: k random partitions of set elements using a suitable sim metric (eq. cosine) dense input id id id idid id sim sort idid id id id id sim id id id id id id sim candidates
- 12. Random projection trees: issues Good. — Good recall, fast queries Slow to generate The bad. — The ugly. — Memory usage Hashing functions generating identical hashes for similar (but not identical) input. Various implementations for different distances: Hyperplane, Cross polytope (cosine), MinHash LSH (Jaccard), … Survey: https://arxiv.org/pdf/1408.2927.pdf Locality sensitive hashing Alternatives. — We use Super-Bit LSH (internal variant, golang) but there’s a wide array of libraries readily available: FALCONN, ANNOY, FLANN, RPFOREST, ..
- 13. The bigger picture client resume file S3 bucket path http://x.x.x.x/parser http request (internal) future(json) connemara (resume parsing, i/o, task orchestration)
- 14. Supporting infrastructure (i/o & conversion) doc->pdf byte array http post conversion service storage pdf byte array screenshot string byte arrayhttp post screenshot service http response (zip with images) storage json {“path”: …”, “url”: “… } Load balancing containerized services via Fabio pdf byte array conversion service conversion service screenshot service
- 15. Supporting infrastructure (i/o & conversion) libreoffice ramdisk golang web server (iris)Docker containers (http micro servers, golang) Deployed via MESOS / Marathon Mesos - kernel abstraction over a cluster (exposes several machines as they would be one) Marathon - Mesos init system Discovery & http load balancing - Consul / Fabio Conversion (document->pdf) service: http://convert.opening.io/doc-to-pdf URL screenshots service: http://convert.opening.io/visitor Conversion (pdf screenshots) service: http://convert.opening.io/pdf-to-img Generic demo: http://engineering.opening.io/demo.html doc to pdf container
- 16. Thank you. https://opening.io @openingdublin founders@opening.io 25 Oxford Lane, Ranelagh Dublin, Ireland, European Union. Catalyser programme