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A Cognitive Substrate for Natural Language Understanding

A Cognitive Substrate for Natural Language Understanding. Nick Cassimatis Arthi Murugesan Magdalena Bugajska. Language & Cognition.

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A Cognitive Substrate for Natural Language Understanding

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  1. A Cognitive Substrate for Natural Language Understanding Nick Cassimatis Arthi Murugesan Magdalena Bugajska

  2. Language & Cognition N. L. Cassimatis, J. Trafton, M. Bugajska, A. Schultz (2004). Integrating Cognition, Perception and Action through Mental Simulation in Robots. Journal of Robotics and Autonomous Systems. Volume 49, Issues 1-2, 30 November 2004, Pages 13-23.

  3. What is difficult? Integration of various sources of information and constraints • Language • Social cues (pointing) • Visual information • Concepts like object • Spatial, physics

  4. Properties of Modular systems: Domain specificity : certain kinds of inputs Informational encapsulation Shallow outputs Why integration is difficult?Fodor’s Modularity of Mind Language Reasoning Sounds ? Mind’s Central Processing ??? Vision Motor Pictures Physical objects

  5. Problems lead to a different goal and Tailored Evaluations Current standards in AI have become: Not sentence understanding or question answering but • Part of speech tagging (98%) • PCFG (Probabilistic Context Free Grammar) • Evaluation Metrics • Precision & Recall – 90% • Exact match – 20- 40% HPSG semantics oriented – 70%

  6. Our approach – Substrate

  7. Non Modular Space Event • Substrate: • Representation • Procedural • Multiple processes • Language is a part of and interacts freely with the greater cognitive system Category Conflict Resolution Identity Focus of Attention (buffer) World Temporal Perception Identity Hypothesis Difference Temporal Constraint N.L. Cassimatis (2006).  A Cognitive Substrate for Human-Level Intelligence.  AI Magazine. Volume 27 Number 2.

  8. Substrate Mappings: The particular substrate : • Physical reasoning : • N. L. Cassimatis (2002). Polyscheme: A Cognitive Architecture for Integrating Multiple Representation and Inference Schemes. Doctoral Dissertation, Media Laboratory, Massachusetts Institute of Technology, Cambridge , MA • Word Learning : • M. Bugajska, N.L. Cassimatis (2006). Beyond Association: Social Cognition in Word Learning.  In Proceedings of the International Conference on Development and Learning. • Social Cognition: • P. Bello, N.L. Cassimatis (2006). Developmental Accounts of Theory-of-Mind Acquisition:  Achieving Clarity via Computational Cognitive Modeling.  In Proceedings of 28thAnnual Conference of the Cognitive Science Society. • P. Bello & N.L. Cassimatis (2006).  Understanding other Minds: A Cognitive Modeling Approach.  In Proceedings of the International Conference on Cognitive Modeling.

  9. Language to Substrate Mapping

  10. HPSG Mapping: A. Murugesan, N.L. Cassimatis (2006). A Model of Syntactic Parsing Based on Domain-General Cognitive Mechanisms. In Proceedings of 28thAnnual Conference of the Cognitive Science Society.

  11. Example of Syntax Semantics interaction

  12. Semantics & Syntax Interaction E. g. : Given a sentence with an ambiguous word – choose the correct interpretation of the word; “The bug needs a battery” animal insect System error listening device bug annoy (verb) Eavesdrop (verb)

  13. Implementation(default) Rules: • By default the most probable [bug - animal insect] is chosen • e.g. of such a sentence : “The bug crawled” Phonology ?phrase ‘bug’ ~~> Lexicon ?phrase animalBug Abnormality predicates are used to prioritize interpretations Phonology ?phrase ‘bug’ + Blocked ?phrase animalBug ~~> Lexicon ?phrase systemBug Phonology ?phrase ‘bug’ + Blocked ?phrase systemBug ~~> Lexicon ?phrase listeningDeviceBug Phonology ?phrase ‘bug’ + Blocked ?phrase listeningDeviceBug ~~> Lexicon ?phrase annoyBug Phonology ?phrase ‘bug’ + Blocked ?phrase annoyBug ~~> Lexicon ?phrase eavesdropBug Blocked ?phrase ?prevLexicon = = > NOT Lexicon ?phrase ?prevLexicon likely!

  14. Implementation(Semantics) Two implicit requirements here : 1. generate semantics of a sentence 2. availability of background information Walk through the example : “The bug needs a battery”

  15. Background knowledge ISA(?obj, Inorganic) = = > ISA(?obj, Physical) ISA(?obj, Organic) = = > ISA(?obj, Physical) ISA(?obj, Inorgainc ) = = > NOT ISA (?obj, Organic) ISA(?obj, Orgainc ) = = > NOT ISA (?obj, Inorganic) Category hierarchy Entity Abstract Physical Organic Inorganic Need ( ?object, ?neededObj) + ISA(?neededObj,battery) = = > ISA(?object, Inorganic)

  16. Conflict in Semantics Lexicon(?phrase,animalBug) + Referent(?phrase, ?phraseRef) = = > ISA(?phraseRef,Organic) According to default rule Lexicon(?phrase,animalBug) is likely true (l,?) Therefore by the above rule ISA(?phraseRef,Organic) is also likely true However once the sentence is formed and Needs(?phraseRef, ?batteryObj) is asserted ; according to background knowledge ?phraseRef must be Inorganic and NOT Organic! i.e. ISA(?phraseRef,Organic) is Certainly false (?,C) animalBug animalBug-1 (l,C) conflict

  17. Contribution A framework for integration • Implausibility of non modular approach is reduced • Learnability of language • Seamless integration

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