pres clarkm5

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Published on October 15, 2007

Author: Melinda


Learning by Reading:  Learning by Reading Micah Clark & Selmer Bringsjord Rensselaer AI & Reasoning (RAIR) Laboratory Department of Cognitive Science Department of Computer Science Rensselaer Polytechnic Institute (RPI) Troy NY 12180 USA 03.20.06 Slide2:  Turning to written text and diagrams to learn, isn’t considered learning as it has been rigorously studied in computer science, cognitive science, and AI. In them, to learn is almost invariably to produce an underlying function f on the basis of a restricted set of pairs. Yet this form of Learning by Reading (LBR) underpins much of modern human life – e.g. the educational system, job training, IRS tax forms, product manuals. Shallow vs. Deep Learning:  Shallow vs. Deep Learning Shallow Learning: Absorb the semantic content explicitly present in the surface structure and form of the medium (texts) Deep Learning: Reflective contemplation of semantic content with respect to prior knowledge, experience, and beliefs as well as imaginative hypothetical projections Example: Book Reports! Shallow LBR for Slate:  Shallow LBR for Slate Reading Process:  Reading Process Process: Intelligence Reports  Multi-Sorted Logic Reading Process Implementation:  Reading Process Implementation Process: Intelligence Reports  Multi-Sorted Logic Reading Process – Phase 1:  Reading Process – Phase 1 ACE (Fuchs, et al) WordNet used prior as lexicon database for CELT, an ACE-like controlled language (Pease, et al) Manual transcription/authoring in controlled languages is viable at scale (Allen & Barthe) Techniques for automated conversion from natural English to controlled English are being developed (Mollá & Schwitter) Attempto Controlled English:  Attempto Controlled English ACE is an unambiguous proper subset of full English Vocabulary of reserved function words and user-defined content words Grammar is context-free, phrase-structured, and definite clause Principles of Interpretation deterministically disambiguate otherwise ambiguous phrases Direct translation into Discourse Representation Structures Reading Process – Phase 2:  Reading Process – Phase 2 ACE Parser (APE) Discourse Representation Structures (DRSs) are central to Discourse Representation Theory (DRT) (Kamp & Reyle) DRT is a linguistic theory for assigning meaning to discourse by sequential additive contribution DRS is a syntactic variant of first-order logic for the resolution of unbounded anaphora DRS is a structure ((referents), (conditions)) DRS Example:  DRS Example “John talks to Mary.” ((A, B), (John(A), Mary(B), talk(A, B))) …“He smiles at her.” ((A, B, C, D), (John(A), Mary(B), talk(A, B), smile(C, D), C=A, D=B)) DRS Example:  DRS Example …“She does not smile at him.” ((A, B, C, D), (John(A), Mary(B), talk(A, B), smile(C, D), C=A, D=B), ((E, F), (smile(E, F), E=B, F=A))) Reading Process – Phase 3:  Reading Process – Phase 3 Transformation from DRS to MSL/FOL is well understood (Blackburn & Bos) ACE uses an extended form of DRS Small, domain-neutral, encoding scheme & ontology to capture semantic content Straight-forward translation would interject ACE’s ontology/encoding scheme Translation must map from ACE’s ontology to another, perhaps PSL Similar to CELT’s mapping of WordNet to the Suggested Upper Merged Ontology (SUMO) Encoding Scheme Examples:  Encoding Scheme Examples Nouns and verbs have semantic type; person, object, time, or unspecified for nouns, event, state, or unspecified for verbs e.g. object(A, named_entity, person) Properties are encoded using property e.g. green(A)  property(A, green) Predicates are encoded using predicate e.g. enter(A, B)  predicate(P, event, enter, A, B) Slate Reading Example:  Slate Reading Example Input Text:  Input Text Security searches every foreigner that boards a plane. Abdul is an Iranian. He boards DL846. Parse Tree:  Parse Tree DRS:  DRS Multi-Sorted Logic:  Multi-Sorted Logic (Using Inverse Encoding Map) 1. A (Security(A)  B,C ((foreigner(B)  plane(C)  board(B, C))  search(A, B))) 2. AB (Abdul(A)  Iranian(A)  DL846(B)  board(A, B)) Comparison to KANI and HITIQA:  Comparison to KANI and HITIQA Knowledge Associates for Novel Intelligence (KANI) High-Quality Interactive Question Answering (HITIQA) Technical Accomplishments:  Technical Accomplishments Proof-of-concept demonstration of automatic translation of a controlled English to FOL for the IA domain Demonstration leverages 3rd party technologies as previously discussed Effort has identified specific aspects of the approach in need of novel research Programmatic Accomplishments:  Programmatic Accomplishments Bringsjord, S. & Clark, M. (2006) ‘For Problems Sufficiently Hard . . . AI Needs CogSci.’ To appear in Proceedings for the American Association for Artificial Intelligence’s Spring Symposium on Cognitive Science and AI (“Between a Rock and a Hard Place: Cognitive Science Principles Meet AI-Hard Problems”). Clark, M & Bringsjord, S. (2006) ‘Learning by Reading’, Invited talk for the Institute for Informatics, Logics, and Security Studies, State University of New York at Albany, Albany, NY. Bringsjord, S. & Clark, M. (2006) ‘Solomon: A Next Generation Q&A System’, Blue-sky proposal in response to BAA N61339-06-R-0034 (DTO AQUAINT Program phase 3). Clark, M. (2006) ‘Method for Detecting Infinite Ambiguity in Context-Sensitive Generative Grammars’, Research Note, Rensselaer AI & Reasoning (RAIR) Laboratory, Cognitive Science Department, Rensselaer Polytechnic Institute, Troy, NY. Future Research:  Future Research Interpretation of ‘natural style’ proofs as DRSs Ontologically neutral DRSs Ambiguous referents and incremental resolution Conversational DRT Non-monotonic transitions in DRT Restatements in Conversational Discourse Immediate Objectives:  Immediate Objectives Develop inverse mapping and translation from ACE ontology and encoding to ‘vanilla’ MSL (with Bettina) Develop basic translation/reformulation of natural deductive proofs (NDL?, Athena?, Slate?) into DRSs (with Sunny) References:  References Allen, J. & Barthe, K. (2004), ‘Introductory Overview of Controlled Languages’, Invited talk for the Society for Technical Communication. Presentation. Blackburn, P. & Bos, J. (Forthcoming), Working with Discourse Representation Theory: An Advanced Course in Computational Semantics. Forthcoming. Fuchs, N. E., Hoefler, S., Kaljurand, K., Schneider, G. & Schwertel, U. (2005), Extended Discourse Representation Structures in Attempto Controlled English, Technical Report ifi-2005.08, Department of Informatics, University of Zurich, Zurich, Switzerland. Fuchs, N. E., Kaljurand, K., Rinaldi, F. & Schneider, G. (2005), A Parser for Attempto Controlled English, Technical Report IST506779/Zurich/I2D3/D/PU, REWERSE. Hoefler, S. (2004), The Syntax of Attempto Controlled English: An Abstract Grammar for ACE 4.0, Technical Report ifi-2004.03, Department of Informatics, University of Zurich, Zurich, Switzerland. Fuchs, N. E., Schwertel, U. & Schwitter, R. (1999), Attempto Controlled English (ACE) Language Manual, Version 3.0, Technical Report 99.03, Department of Computer Science, University of Zurich, Zurich, Switzerland. ISO (2001), Industrial automation system and integration — Process specification language, Committee Draft ISO/CD 18629-1, International Organization for Standardization (ISO). Kamp, H. & Reyle, U. (1993), From Discourse to Logic: Introduction to Model-theoretic Semantics of Natural Language, Formal Logic and Discourse Representation Theory, 1 edn, Springer. Mollá, D. & Schwitter, R. (2001), From Plain English to Controlled English, in ‘Proceedings of the 2001 Australasian Natural Language Processing Workshop’, Macquarie University, Sydney, Australia, pp. 77–83. Pease, A. & Fellbaum, C. (2004), Language to Logic Translation with PhraseBank, in ‘Proceedings of the Second International WordNet Conference (GWC2004)’, Masaryk University Brno, Czech Republic, pp. 187–192. Pease, A. & Murray, W. (2003), An English to Logic Translator for Ontology-based Knowledge Representation Languages, in ‘Proceedings of the 2003 IEEE International Conference on Natural Language Processing and Knowledge Engineering’, Beijing, China, pp. 777–783.

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