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Natural Intelligence ICCS 2010

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Fatih Mehmet Güler - Natural Intelligence ICCS 2010 paper presentation

Fatih Mehmet Güler - Natural Intelligence ICCS 2010 paper presentation

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  • 1. Natural Intelligence -Commonsense Question Answeringwith Conceptual Graphs Fatih Mehmet Güler and Aysenur Birturk Department of Computer Engineering, METU 06531, Ankara/TURKEY fmguler@gmail.com birturk@ceng.metu.edu.tr
  • 2. Motivation Massive Knowledge found as Natural Language Text based Question Answering (no tagging) Open Domain Question Answering Address Commonsense Reasoning Problem Linguistically motivated KRR  Intelligence is the accumulation of knowledge Integrate State of the Art Tools Ultimate goal: Getting closer to strong AI
  • 3. Summary of the System Natural Language is parsed Utterances are represented using CGs Concepts and Relation types are mapped to Cyc equivalent counterparts Type hierarchies are computed Knowledge is accumulated If the input is a question  Search for answer (projection)
  • 4. Summary of the System (Cont’d) NI NLP KRR Commonsense CCG CGs Open Cyc C&C Tools Cogitant
  • 5. Background Combinatory Categorial Grammar (CCG)  C&C Tools Conceptual Graphs  Cogitant Open Cyc
  • 6. Combinatory Categorial Grammar (CCG) Lexicalized Theory of Grammar based on Categorial Grammar ( Steedman 2001).  Functions can be applied or composed  Arguments can be picked up or turned into functors (Type raising) Easy for Semantic Representations  Small number of semantically transparent combinatory rules to combine CCG categories.  Assign semantic representations to the lexical entries  Interpret combinatory rules
  • 7. CCG parse for “Mr. Hyde ate twolemmons”
  • 8. CCG Parse for “Susan knows that Boblikes Fred”
  • 9. DRS for “Susan knows that Bob likesFred”
  • 10. C&C Tools Linguistically Motivated Large-Scale NLP with C&C and Boxer. (Curran, Clark, Bos, 2007)  C&C Parser  POS Tagging, Supertagging  Parsing, Chunking  Named Entity Recognition  Boxer  Uses CCG parser output  Generates DRS Semantic Representations Freely available for research  http://svn.ask.it.usyd.edu.au/trac/candc/wiki
  • 11. C&C Tools Large Scale NLP is possible with C&C and Boxer C&C Parser: state of the art parser for CCG Boxer: Semantic representations in DRS
  • 12. Open Cyc Open source version of Cyc system Cyc: greatest effort to encode Common Sense knowledge in machine processable way 500.000 concepts 26.000 relations and 5.000.000 assertions CycL language similar to Lisp We use Cyc to map parsed words to common sense counterparts such as person to #$Person (disambiguation)
  • 13. Open Cyc (cont’d) (#$likesAsFriend #$GeorgeWBush #$AlGore) #$isa, #$genls (#$isa #$GeorgeWBush #$UnitedStatesPresident) (#$genls #$UnitedStatesPresident #$Person)
  • 14. Cogitant Library for Conceptual Graph operations Supports broad CG operations (Genest & Salvat, 1998)  Graph representation  Conversion from CGIF  Projection checking  Rule application
  • 15. Natural Intelligence – CommonsenseQuestion Answering with CGs Augment Common Sense knowledge Modular Approach  Separation of Concerns  State of the art tools
  • 16. Architecture - Modules Natural Language Processing (C&C Tools are used for implementation)  Convert natural language to CGIF Reasoning (Cogitant library is used for implementation)  CG operations Common Sense (Open Cyc is used for implementation)  Common sense mapping Storage (Conceptual Graphs are stored in a database)  Persistence of CGs
  • 17. System Definition User enters a sentence from web interface; This sentence is converted to CGIF using the NLP module; CGIF is converted to CGs using the reasoning module; Support is generated to CGs using the common sense module; Common sense rules gathered from common sense module are applied to CGs using reasoning module; CGs are merged to the previous ones using reasoning module; If the input sentence is a question sentence, same operations take place, except the resulting graph is used to query existing CGs using the reasoning service, and if there are projections from this query graph to previous CGs, results are displayed to the user; CGs are persisted using the storage module.
  • 18. Common Sense Mapping Cyc: (prettyString TERM STRING) Chain up to #$Thing using #$genls relations Same for relations using #$genlPreds Relation hierarchies are converted to forward rules  #$performedBy -> #$temporallyRelated
  • 19. Sample Concept Hierarchy #$Place -> #$EnduringThing-Localized -> #$Location-Underspecified -> #$Thing -> #$SomethingExisting -> #$Individual -> #$Thing ^^ #$Trajector-Underspecified -> #$Location-Underspecified ^^ #$TemporallyExistingThing -> #$TemporalThing -> #$Individual ^^ #$SpatialThing-Localized -> #$TemporallyExistingThing ^^ #$SpatialThing -> #$Individual ^^ #$Boundary-Underspecified -> #$Region-Underspecified -> #$Location-Underspecified ^^ #$Landmark-Underspecified -> #$Individual ^^ #$Location-Underspecified ^^ #$SpatialThing-NonSituational -> #$SpatialThing ^^ #$Individual ^^ #$Location-Underspecified ^^
  • 20. Conversion to Cogitant Support Convert Cyc hierarcy to Cogitant support format  Concept Types  Relation Types  Individuals  Rules Convert assertions to Cogitant graph format Apply forward rules
  • 21. Answering Queries
  • 22. Significance Sentences like;  What are the intangible things in this situation?  Was Mr. Hyde there while eating the apples?  Does Mr. Hyde exist after eating the apples?  Do the apples exist after Mr. Hyde ate them? Deep Natural Language Understanding State of the art tools Open domain question answering
  • 23. Difficulties Open Cyc API is broken  Does not work in Turkish locale (fixes are sent to maintainers)  Still, provided API sends one IP packet per character, way too slow over network  Custom socket API is developed and used over TCP  Custom Lisp functions for generalization hierarchy and concept mapping Cogitant problematic  Java API is very limited (compared to C++)  Only works over XML files
  • 24. Conclusion Central Integrated Common Sense QAS CCG for Natural Language Processing Conceptual Graphs for KRR Cyc for Common Sense
  • 25. Future Work Implement Rule Induction Backward Chaining (Resolution) Improve NLP module and Common Sense mapping Probabilistic Reasoning Question Answering System (QAS) to be used in;  Education (Learning Management Systems)  Semantic Search (Content Management Systems)  Intelligent Help

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