From Text To Reasoning - Marko Grobelnik - SWANK Workshop Stanford - 16 Apr 2014

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text understanding topic - ...from plain text to logic

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From Text To Reasoning - Marko Grobelnik - SWANK Workshop Stanford - 16 Apr 2014

  1. 1. From Text to Reasoning Marko Grobelnik Jozef Stefan Institute / Cycorp Europe, Slovenia SWANK Workshop, Stanford, Apr 16th 2014Thanks to Michael Witbrock, Janez Starc, Luka Bradesko, Blaz Fortuna
  2. 2. Reflection on what should be the goal of NLP • The (mostly) forgotten long term aim of NLP is to understand the text • …and not so much ‘processing’ itself (as NLP suggests) • The curse of shallow solutions working well enough for too many problems, made people (and researchers) happy for too long • …as much as information retrieval and text mining are useful, they delayed development of “text understanding”
  3. 3. Language vs. World • …if we agree with the above statement, then at this point in time, we have ‘language’, but the ‘world’ is more or less missing • So – so what a ‘world’ or ‘world model’ could be?
  4. 4. CYC KNOWLEDGE BASE Thing Universe isa isa Celestial Body isa located in Planet subclass Earth isa Animal isa Human subclas s Physics Money Mathematics Chemistry Time Learning FoodVehicles Event Education School Language LoveEmotions Going for a walk Death Cat Euro Working Words Driving RainStabbing someone Nature Tree Hatred Fear Physics Time Learning Vehicles Event Education School Emotions Going for a walk Death Cat EuroWords Driving Rain Stabbing someone Nature Tree Hatred Fear Planet Earth isa Human Physics Money Mathematics Chemistry Time Learning FoodVehicles Event Education Languag e LoveEmotions Going for a walk Cat Euro Working Words Driving Rain Tree Hatred Fear Learning Vehicles Event Education School Emotions Euro Driving Stabbing someone Hatred Fear Creating a World Model (top-down approach - Cyc)
  5. 5. Model of the world… • …beyond surface knowledge • …to interconnect contextualized fragments Why? • To make reasoning capable of connecting isolated fragments of knowledge • To derive new knowledge beyond materialized factual knowledge World model Top-down KA Bottom-up KA Multimodal data Why we need a World model?
  6. 6. Disambiguation with a world model (CycKB)World model used as a set of common-sense semantic constraints to disambiguate text
  7. 7. One of the challenges for the future: Micro-reading • It is “easier” to understand millions of documents than one document • …reading and understanding a single document is micro-reading • The following experiment is on how much knowledge we can extract from individual documents • …extraction is in a form of first order inferentially productive Cyc logic • …allowing us full reasoning to identify new facts • …minimizing human involvement, optimizing precision and recall Document Assertions Reasoning Dialogue
  8. 8. Example of text and extracted Cyc assertions (1/2) Automatically Extracted Assertions: • (isa ?V1 ProsecutingEvent) • (agent ?V1 RudyGiuliani) • (genls Entity Agent) • (isa RudyGiuliani Agent) • (isa RudyGiuliani Entity) • (isa ?V3 OrganizingEvent) • (patient ?V3 (IntersectionFn OrganizedCrime WallStreet)) • (isa (IntersectionFn OrganizedCrime WallStreet) Patient) • (genls Entity Patient) • (isa OrganizedCrime Patient) • (isa OrganizedCrime Entity) • (isa WallStreet Patient) • (isa WallStreet Entity) Sentence: He prosecuted a number of high-profile cases, including ones against organized crime and Wall_Street financiers.
  9. 9. Example of text and extracted Cyc assertions (2/2) Automatically Extracted Assertions: • (isa ?V1 SubstitutingEvent) • (temporal ?V1 Lincoln) • (genls Entity Agent) • (isa Lincoln Agent) • (genls Person Entity) • (isa Lincoln Entity) • (isa Lincoln Person) • (isa ?V3 SucceedingEvent) • (temporal ?V3 Grant) • (isa Grant Agent) • (isa Grant Entity) • (isa Grant Person) Sentence: Each time a general failed, Lincoln substituted another until finally Grant succeeded in 1865.
  10. 10. Reasoning on extracted assertions (Cyc) Query: (and (isa ?Per Person) (birthDate ?Per ?BD) (occursBefore ?BD WorldWarII) (thereExistsAtLeast 2 ?Role (lifeRole ?Per ?Role) (roleInIndustry ?Role FilmIndustry) ) ) Answers: Sir Derek_George_Jacobi Sir Alexander_Korda Victor Lonzo_Fleming John_Francis_Junkin Cornel_Wilde George_Stevens Bertrand_Blier NL Query: People born before World War II who had at least two roles in the film industry KB?
  11. 11. Knowledge Capture Knowledge Use Rule: (implies (and (isa ?VENUE FoodTruck-Organization) (lastVenue ?USER ?VENUE) (suggestionsForCuriousCatQuestionType FoodTruckSecondaryTypeOfPlace- CuriousCatQuestion ?SUGGESTIONLIST)) (curiousCatWantsToAskUser ?USER (secondaryTypeOfPlace ?VENUE FoodTruck-Organization ?TYPE) ?SUGGESTIONLIST)) Witbrock, M., Bradeško, L., 2013, Conversational Computation in Michelucci, Pietro (Ed.) Handbook of Human Computation, 531-543. Intelligent SIRI: http://curiouscat.cc/
  12. 12. Some of the AI challenges for next years • Background knowledge in a form of a World Model • …to have knowledge contextualized • Representing and scalable reasoning knowledge with operational soft logic • …to decrease brittleness of logic and increase scale • Economically viable structured knowledge acquisition with high precision and recall • …to increase the reach of what we can acquire • Emphasizing understanding vs. applying black box models

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