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Stefan Geißler | Hybrid semantic document enrichment using machine learning and linguistics - The Cogito Studio

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http://2016.semantics.cc/stefan-gei%C3%9Fler

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Stefan Geißler | Hybrid semantic document enrichment using machine learning and linguistics - The Cogito Studio

  1. 1. Hybrid semantic document enrichment using machine learning and linguistics Stefan Geißler, SEMANTICS, Leipzig Sept 14 2016 Expert System
  2. 2. • Title What is this? A graph showing the distribution of large cities in the world Size of the city (population) The city‘s rank
  3. 3. • Title What is this? A graph showing the richest people of the world Wealth of the person The person‘s rank
  4. 4. • Title What is this? A graph showing the most frequent words from a large text corpus Frequency of the word The word‘s rank
  5. 5. Empirical evidence: Many types of data from physics, social sciences etc follow such a distribution „Zipf‘s law“: The number of data points (cities, rich people, words) with a value higher than S (on the y axis) is proportional to 1/S.
  6. 6. • Title Distribution of categories in many categorized/tagged corpora Frequency of the category The category‘s rank
  7. 7. Problem #1: How does that fit the requirement at the start of many categorization projects that a category will need a decent amount of data (>100 documents) to be trained? Larger categories can be trained (learned automatically) smaller ones often can‘t.
  8. 8. Problem #2: Even for the frequent enough categories: Is a training corpus really representative? Is „Greece“ always about „debt crisis“? Is „Ansbach“ always about „terror“? Learning method may learn unwanted associations
  9. 9. • Title Solution? More data? No because, - The graph here is scale-free - More data is often not available or very costly Frequency of the category The category‘s rank
  10. 10. Solution: Let the human expert refine the automatically created model Human document categorization: If („Etna“ or „Vesuv“ or „Pinantubo“) AND („lava“ or „eruption“) Then „Volcanism“ Machine document categorization:
  11. 11. This is seldomly a subject in scientific work on document categorization. Different classification methods most often compared only on the basis of their (automatic) performance on a evaluation corpus
  12. 12. … but this is often a requirement in real-world document categorization projects. • Training corpora alone are often not enough to attained expected levels of quality. • Additional data hard to find (manual preparation or curation very costly) • Existing corpora may not always be representative.
  13. 13. Our suggestion • Use available training data to train a model • Make the model available in a human readable formal language • Allow user to inspect and refine model where needed in a dedicated developement&testing environment
  14. 14. • A rich formal language (strings, lemmas, regexps, semantic concepts, operators …) allows to express learnt associations for bag of words models • … as well as detailed syntactic/semantic constraints • … and visualize and evaluated the result in the same application
  15. 15. • For the reasons explained above, the statistical learning approach may erroneously learn a rule that the words „Athens“ or „Greece“ allone justify assigning the document to „Banking Crisis“ • The user can refine the learnt rule, adding the further constraint that features like „Debt“, „Schäuble“ or „Troika“ are required before the category is assigned.
  16. 16. … Sample projects • <US Media company> • Large category schema for news articles • Task: set up solution that allows combining automatically created rule sets with manual refinement • <Insurance company> • Categorize medical reports using ICD category scheme • Go beyond quality that can be attained by using only the manually coded training set
  17. 17. Conclusion • Requirements in categorization projects in the industry are sometimes not identical to the scenarios in academic categorization benchmarks • Available training data sometimes limited even in the age of big data • Allow the seamless (one language, one development environment) application of both learnt as well as manually crafted rules
  18. 18. Expert System Who we are
  19. 19. Expert System: Largest European provider of pure semantic technologies • 7 Geographies • 250+ team members • Listed on the AIM exchange • Recommended by Gartner, Forrester, IDC ... • Experiences from hundreds of projects • Award winning technology: Taxonomy / Ontology Management, NLP, Information extraction, Question Answering, Cognitive Computing
  20. 20. Global Positioning – Selected Clients 21 ENERGY, OIL & GAS GOVERNMENT FEDERAL AGENCIES MEDIA & PUBLISHING Life Sciences FINANCE

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