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Deep learning for text analytics
- 1. (Technical) Big Data Analytics for non-technical end-users ErikTromp – CEO UnderstandLing
- 2. Contents • (Short) into • Rationale • Tuktu platform • Deep learning for computational linguistics • CEMistry – Customer Experience Monitoring on steroids
- 3. (Short) Intro • Big data science experts • Specialisms • Computational Linguistics • Customer Experience Management • Service: strategic advices all the way to operational implementation • Own platform:Tuktu • Soon: own product:CEMistry • Trainings/education on big data science
- 4. (Short) Intro Quantify every touchpoint of a customer with your company 4 major areas • Text Analytics • Web Analytics • Mobile Analytics • CRM/Backend Analytics
- 5. (Short) Intro • ErikTromp • Age: 28 • CEO UnderstandLing • Graduated on Sentiment Analysis in 2011 • Multilingual Sentiment Analysis on Social Media • Software engineer – Scala • Machine learning • Author of platformTuktu
- 6. Contents • (Short) into • Rationale • Tuktu platform • Deep learning for computational linguistics • CEMistry – Customer Experience Monitoring on steroids
- 7. Rationale Big data science allows to utilize opportunities
- 8. Rationale Big data science allows to utilize opportunities Big data science drives business
- 9. Rationale Big data science allows to utilize opportunities Big data science drives business But is very much a technical revolution, with business implications
- 10. Rationale Many companies want to utilize the opportunities big data science brings
- 11. Rationale Many companies want to utilize the opportunities big data science brings These companies do not have sufficient capabilities to do so
- 12. Rationale Many companies want to utilize the opportunities big data science brings These companies do not have sufficient capabilities to do so Nor are there many suppliers that can do tech, analytics and know their business
- 13. Rationale But these companies often do have their own (business) analysts
- 14. Rationale IDEA Make big data science accessible to non-technical users
- 15. Contents • (Short) into • Rationale • Tuktu platform • Deep learning for computational linguistics • CEMistry – Customer Experience Monitoring on steroids
- 17. Tuktu – Early Days • Started off as a personal project to make life easier • Out of a collaboration with the Maastricht University • Idea: save time on coding/engineering, focus on logic and functionalities
- 18. Tuktu – Early Days • Started off as a personal project to make life easier • Out of a collaboration with the Maastricht University • Idea: save time on coding/engineering, focus on logic and functionalities Instead of writing code over and over again, have it present and configure its building blocks
- 19. Tuktu – Early Days • Started off as a personal project to make life easier • Out of a collaboration with the Maastricht University • Idea: save time on coding/engineering, focus on logic and functionalities Instead of writing code over and over again, have it present and configure its building blocks In a visual and straightforward way!
- 20. Tuktu – Now Your one-stop shop for everything big data science
- 21. Tuktu – Now • Realtime and batch processing • Synchronous and asynchronous processing • REST API • Drag-and-drop modelling of jobs • Distributed file system:TDFS • Key/value-sture:TuktuDB • Real-time visualization • Web analytics support • Scheduling • No master/slave architecture • Local or distributed computing • Machine learning • Deep learning • Cross-platform due to JVM • Easy installation: just unzip!
- 22. Tuktu DEMO
- 23. Contents • (Short) into • Rationale • Tuktu platform • Deep learning for computational linguistics • CEMistry – Customer Experience Monitoring on steroids
- 24. Deep Learning for Computational Linguistics IDEA Learn language models generically
- 25. Deep Learning for Computational Linguistics IDEA Learn language models generically Model every CL-problem on top on the generic model
- 26. Deep Learning for Computational Linguistics This way, we can do almost any task on almost any language Without too much/with less effort
- 27. Deep Learning for Computational Linguistics How?
- 28. Deep Learning for Computational Linguistics There are many linguistics resources available Sadly; most is for English In particular: AnnotatedTreebanks for deep parsing
- 29. Deep Learning for Computational Linguistics We can use this however
- 30. Deep Learning for Computational Linguistics 1. Co-train word vectors for target language and English
- 31. Deep Learning for Computational Linguistics 1. Co-train word vectors for target language and English 2. Train parsing models on English language
- 32. Deep Learning for Computational Linguistics 1. Co-train word vectors for target language and English 2. Train parsing models on English language 3. Co-finetune models on co-trained word vectors
- 33. Deep Learning for Computational Linguistics 1. Co-train word vectors for target language and English 2. Train parsing models on English language 3. Co-finetune models on co-trained word vectors 4. Pre-train (recursive) auto-encoder using parsing model for target language
- 34. Deep Learning for Computational Linguistics 1. Co-train word vectors for target language and English 2. Train parsing models on English language 3. Co-finetune models on co-trained word vectors 4. Pre-train (recursive) auto-encoder using parsing model for target language 5. Use recursive auto-encoder for specific task in target language • Topic detection, sentiment analysis, named entity recoginition, authorship profiling
- 35. Deep Learning for Computational Linguistics DEMO Unsupervised parsing in Dutch
- 36. Contents • (Short) into • Rationale • Tuktu platform • Deep learning for computational linguistics • CEMistry – Customer Experience Monitoring on steroids
- 37. CEMistry TEXT ANALYTICS MOBILEANALYTICS WEB ANALYTICS BACKEND/CRM Customer Profile Tuktu.js Visitor Customer Events Page views “SDK” Visitor Customer Events AppTriggers User Customer Collectors Events Communication (NLP) (Database) Connectors Customer Events Transactions
- 38. Questions?
- 39. ErikTromp CEO UnderstandLing erik@understandling.com http://www.understandling.com http://www.tuktu.io http://www.linkedin.com/in/eriktromp https://github.com/UnderstandLingBV/Tuktu Talk to us on Gitter! https://gitter.im/UnderstandLingBV/Tuktu