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Robot Babies

What can they teach us about language acquisition?

  • Chapter
Ecology of Language Acquisition

Part of the book series: Educational Linguistics ((EDUL,volume 1))

Abstract

Artificial Intelligence, like traditional linguistics and computational linguistics, tends to treat language as an isolated phenomenon, and syntax and semantics as separate areas of study. The approach taken to developing grammars, parsers and natural language systems has been more like the dissection of a cadaver than a study of live interactions in a complex ecology: the basic anatomical structure can be discovered and some educated guesses made about the physiological interactions, but there is no hope of understanding the functional operation of a system that involves multiple individuals living in a complex environment. In fact, natural language research has been even more hampered, as computer scientists ignored the collective wisdom of linguistics, psychology, and sociology, and relied on their own intuitions rather than real world data about language in action.

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References

  • Block, H. D., Moulton J., & Robinson G. (1975). Natural language acquisition by a robot. International Journal of Man-Machine Studies, 7, 571–608.

    Article  Google Scholar 

  • Bod, R. (1995). Enriching linguistics with statistics: Performance models of natural language. ILLC PhD Dissertation, University of Amsterdam.

    Google Scholar 

  • Boy, J. (1977). Dechiffrierungsalgorithmen zur phonetischen Identifikation von Buchstaben. Magister Dissertation, Universität Bochum. Bochum: Studienverlag Brockmeyer.

    Google Scholar 

  • Bregman, A. (1990). Auditory scene analysis: The perceptual organisation of sound. Cambridge, MA: MIT Press.

    Google Scholar 

  • Brent, M. R. (1997). A unified model of lexical acquisition and lexical access. Journal of Psycholinguistic Research, 26, 363–375.

    Article  Google Scholar 

  • Brooks, R., Breazeal, A., Marjanovic, M., Scassellati, B. & Williamson, M. (1998). The COG project: building a humanoid robot. In C. L. Nehaniv (Ed.), Computation for metaphors, analogy and agents. New York: Springer-Verlag.

    Google Scholar 

  • Brooks, R. A. & Steels, L. (1994). The artificial life route to artificial intelligence: Building embodied situated agents. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Chan, R. (1988). Concept learning by computer: Simple movement. B.Sc. Computer Science Honours Thesis, Macquarie University, Sydney, AUS.

    Google Scholar 

  • Entwisle, J. & Groves, M. (1994). A method of parsing English based on sentence form. In Daniel Jones (Ed.), New methods in language processing, (pp. 116–122). Centre for Computational Linguistics, University of Manchester, UK.

    Google Scholar 

  • Entwisle, J. (1997). A constraint parser for English. Computer Science PhD dissertation, Flinders University of South Australia, Adelaide, AUS.

    Google Scholar 

  • Feldman, J. A., Lakoff, G., Stolcke, A., & Hollback Weber, S. (1990). Miniature language acquisition: A touchstone for cognitive science (TR-90–009). International Computer Science Institute, Berkeley, USA.

    Google Scholar 

  • Finch, S. (1993). Finding structure in language. PhD dissertation, University of Edinburgh, UK.

    Google Scholar 

  • Gold, E. M. (1967). Language identification in the limit. Information and Control, 10, 447–474.

    Article  Google Scholar 

  • Grünwald, P. (1996). A minimum description length approach to grammar inference. In S. Wermter, E. Riloff, & G. Scheler (Eds.), Connectionist, statistical, and symbolic approaches to learning for natural language processing. New York: Springer-Verlag.

    Google Scholar 

  • Harris, Z. (1960). Structural linguistics. University of Chicago Press.

    Google Scholar 

  • Hewson, J. (1991). Determiners as heads. Cognitive Linguistics, 2 (4), 317–337.

    Article  Google Scholar 

  • Hogan, J. M., Diederich, J. & Finn, G.(1998). Selective attention and the acquisition of spatial semantics. In D. Powers (Ed.), New methods in language processing and computational natural language learning, (NeMLaP-3/CoNLL-98) (pp. 235–244). New Brunswick, NJ: Association for Computational Linguistics.

    Google Scholar 

  • Homes, D. (1998). Perceptually grounded language learning. B.Sc. Computer Science Honours Thesis, Flinders University, Adelaide, AUS.

    Google Scholar 

  • Horning, J. J. (1969). A study of grammatical inference. PhD Computer Science dissertation, Stanford University .

    Google Scholar 

  • Huang, J. & Powers, D. (2001). Large-scale experiments on correction of confused words. In M. Oudshoom (Ed.), Australian Computer Science Conference. Bond University, Queensland AUS.

    Google Scholar 

  • Hume, D. (1984). Creating interactive worlds with multiple actors. B.Sc. Computer Science Honours Thesis, University of New South Wales, Sydney, AUS.

    Google Scholar 

  • Kozima, H & Ito, A. (1998). Towards language acquisition by an attention-sharing robot. In D. Powers (Ed.), New methods in language processing and computational natural language learning, (NeMLaP-3/CoNLL-98) (pp. 235–244). New Brunswick, NJ: Association for Computational Linguistics.

    Google Scholar 

  • Kohonen, T. (1982). Analysis of a simple self-organizing process. Biological Cybernetics, 44, 135–140.

    Article  Google Scholar 

  • Kuczaj, S. (1983). Crib speech and language play. New Brunswick, NJ: Springer-Verlag.

    Book  Google Scholar 

  • Langacker, R. (1997). Constituency, dependency and conceptual grouping. Cognitive Lingustics,8 (1), 1–32.

    Article  Google Scholar 

  • Lewis, T. (2000). Audio-visual speech recognition: Extraction, recognition and integration. B.Sc. Computer Science Honours Thesis, Flinders University, Adelaide, AUS.

    Google Scholar 

  • Lewis, T. & Powers, D. (2000). Audio-visual speech recognition using red exclusion. Retrieved from VIP (Visual Information Processing) intranet (URL: https:llvip.sc.edu/).

    Google Scholar 

  • Lewis, T. & Powers, D. (2001). Lip feature extraction using red exclusion. In P. Eades & J. Jin (Eds.), Conferences in research and practice in information technology: Visualisation 2000, vol 2.

    Google Scholar 

  • Li, Y., Powers, D. & Peach, J. (2000). Comparison of blind source separation algorithms. In N. Mastorakis (Ed.), Advances in neural networks and applications (pp. 18–23). World Scientific Engineering Society.

    Google Scholar 

  • Li, Y, Powers, D. & Wen, P. (2001). Separation and deconvolution of speech using recurrent neural networks. In H. R. Arabnia (Ed.) Proceedings of the International Conference on Artificial Intelligence (IC -AI01), June 25–28, 2001, Las Vegas, Nevada, USA, (pp. 1303–1309).

    Google Scholar 

  • Malsburg, C. von der (1973). Self-organization of orientation selective cells in the striate cortex. Kybernetik, 14, 85–100.

    Article  Google Scholar 

  • McCarthy, J., Earnest, L., Reddy D. & Vicens, P. (1968). A computer with hands, eyes and ears. American Federation of Information Processing Societies (AFIPS) Conference Proceedings of the Fall Joint Computing Conference 33, I, 329–338.

    Google Scholar 

  • Mehler, J., Jusczyk, P., Lambertz, G., Halsted, N., Bertoncini, J., & Amiel-Tison, C. (1992). A precursor of language acquisition in young infants. Cognition, 29, 143–178.

    Article  Google Scholar 

  • Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for nrncessing information. Psvchological Review. 63, 81–97.

    Article  Google Scholar 

  • Moulton, J. & Robinson, G. (1981). The organization of language. Cambridge University Press.

    Book  Google Scholar 

  • Movellan, J. & Mineiro (1998). Robust sensor fusion: Analysis and application to audio visual speech recognition. Machine Learning, 32, 85–100.

    Article  Google Scholar 

  • Piaget, J. (1955). The language and thought of the child. University of Geneva Press.

    Google Scholar 

  • Piaget, J. (1971). Psychology and epistemology: Towards a theory of knowledge. Viking Press.

    Google Scholar 

  • Pike, K. L. (1949). Phonemics. University of Michigan Press.

    Google Scholar 

  • Pike, K. L. (1967). Language in relation to a unified theory of the structure of human behavior. The Hague: Mouton.

    Google Scholar 

  • Pike, K. L. & E. G. Pike (1977). Grammatical analysis. Summer Institute of Linguistics and University of Texas.

    Google Scholar 

  • Powers, D. & Turk, C. (1989). Machine learning of natural language. New Brunswick, NJ: SpringerVerlag.

    Book  Google Scholar 

  • Powers, D. (1983). Neurolinguistics and psycholinguistics as a basis for computer acquisition of natural language. SIGART (Association for Computing Machinery’s Special Interest Group for Artificial Intelligence) 84, 29–34.

    Google Scholar 

  • Powers, D. (1984). Natural language the natural way. Computer Compacts 2: 100–104.

    Article  Google Scholar 

  • Powers, D. (1991). How far can self-organization go? Results in unsupervised language learning. In D. Powers & L. Reeker (Eds.), AAAI Spring Symposium on Machine Learning of Natural Language and Ontology (DFKI D-91–09), 131–137. University of Kaiserslautern, RFG.

    Google Scholar 

  • Powers, D. (1992). On the significance of closed classes and boundary conditions: Experiments in machine learning of natural language. In D. Powers & W. Daelemans (Eds), SHOE Workshop on Extraction of Hierarchical Structure (ITK Proceedings 92/1), 245–266. Tilburg University, NL.

    Google Scholar 

  • Powers, D. (1997a). Unsupervised learning of linguistic structure: an empirical evaluation. International Journal of Corpus Linguistics, 2(1), 91–131.

    Article  Google Scholar 

  • Powers, D. (1997b). Learning and application of differential grammars, In T. Ellison (Ed), CoNLL97: ACL Workshop on Computational Natural Language Learning, Madrid, July 1997, 88–96.

    Google Scholar 

  • Powers, D. (2001). The robot baby meets the intelligent room. AAAI Spring Symposium on Learning Grounded Representations, Stanford USA, April 2001, 59–62.

    Google Scholar 

  • Ritter, H. & Kohonen, T. (1990). Learning semantotopic maps from context. International joint conference on neural networks. vol. I (pp. 23–26). Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Schifferdecker, G. (1994). Finding structure in language. Diplom Informatik Thesis, University of Karlsruhe, Karlsruhe, FRG.

    Google Scholar 

  • Shi, R., Werker, J., & Morgan, J. (1999). Newborn infants’ sensitivity to perceptual cues to lexical and grammatical words. Cognition, 72, B 11–21.

    Article  Google Scholar 

  • Silverstein, M. (1976). Case marking and the nature of language. Australian Journal of Linguistics, 1, 227–244.

    Article  Google Scholar 

  • Steels, L. & Brooks, R. (Eds.) (1995). Building situated embodied agents: The Alife route to AI. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Steels, L. (1996). A self-organizing spatial vocabulary. Artificial Life Journal, 3(2), 47–63.

    Google Scholar 

  • Steels, L. (1997). Constructing and sharing perceptual distinctions. European Conference on Machine Learning.

    Google Scholar 

  • Turk, C. (1984). A correction natural language mechanism. ECAI-84: Advances in Artificial Intelligence, 225–226. New York: Elsevier.

    Google Scholar 

  • Turing, A. (1936/7). On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, Series 2, 42, 230–265; 43, 433–546.

    Google Scholar 

  • Turing, A. (1950). Computing machinery and intelligence. Mind, 59, 433–460.

    Article  Google Scholar 

  • Turing, A. (1952). The chemical basis of morphogenesis. Philosophical Transactions of the Royal Society, 237, 5–72.

    Article  Google Scholar 

  • Winograd, T. (1973). Understanding natural language. New York: Academic Press.

    Google Scholar 

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© 2002 Springer Science+Business Media Dordrecht

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Powers, D. (2002). Robot Babies. In: Leather, J., van Dam, J. (eds) Ecology of Language Acquisition. Educational Linguistics, vol 1. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-0341-3_9

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  • DOI: https://doi.org/10.1007/978-94-017-0341-3_9

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-6170-6

  • Online ISBN: 978-94-017-0341-3

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