SEARCH

SEARCH BY CITATION

References

  • Abbot-Smith, K., & Tomasello, M. (2006). Exemplar-learning and schematization in a usage-based account of syntactic acquisition. The Linguistic Review, 23, 275290.
  • Ambridge, B., Rowland, C., & Pine, J. (2008). Is structure dependence an innate constraint? New experimental evidence from chilren’s complex-question production. Cognitive Science, 32, 222255.
  • Anderson, J. (2006). Structural analogy and universal grammar. Lingua, 116, 601633.
  • Barlow, M., & Kemmer, S. (Eds.) (2000). Usage-based models of language. Stanford, CA: CSLI Publications.
  • Billot, S., & Lang, B. (1989). The structure of shared forests in ambiguous parsing. In Proceedings ACL 1989 (pp. 143151). Stroudsburg, PA: Association for Computational Linguistics.
  • Bod, R. (1992). A computational model of language performance: Data-oriented parsing. In Proceedings COLING 1992 (pp. 855859). Stroudsburg, PA: Association for Computational Linguistics.
  • Bod, R. (1998). Beyond grammar: An experienced-based theory of language. Stanford, CA: CSLI Publications.
  • Bod, R. (1999). Context-sensitive spoken dialogue processing with the DOP model. Natural Language Engineering, 5, 309323.
  • Bod, R. (2000). Parsing with the shortest derivation. In Proceedings COLING 2000 (pp. 6975). Stroudsburg, PA: Association for Computational Linguistics.
  • Bod, R. (2001). Sentence memory: Storage vs. computation of frequent sentences. Paper presented at CUNY conference on sentence processing, Philadelphia.
  • Bod, R. (2002a). A unified model of structural organization in language and music. Journal of Artificial Intelligence Research, 17, 289308.
  • Bod, R. (2002b). Memory-based models of melodic analysis: Challenging the gestalt principles. Journal of New Music Research, 31, 2737.
  • Bod, R. (2003). Do all fragments count? Natural Language Engineering, 9, 307323.
  • Bod, R. (2006a). Exemplar-based syntax: How to get productivity from examples. The Linguistic Review, 23, 291320.
  • Bod, R. (2006b). An all-subtrees approach to unsupervised parsing. In Proceedings ACL 2006 (pp. 865872). Stroudsburg, PA: Association for Computational Linguistics.
  • Bod, R. (2007a). Is the end of supervised parsing in sight? In Proceedings ACL 2007 (pp. 400407). Stroudsburg, PA: Association for Computational Linguistics.
  • Bod, R. (2007b). A linguistic investigation into U-DOP. In Proceedings of the workshop on cognitive aspects of computational language acquisition ACL 2007 (pp. 18). Stroudsburg, PA: Association for Computational Linguistics.
  • Bod, R., & Kaplan, R. (1998). A probabilistic corpus-driven model for lexical-functional analysis. In Proceedings ACL 1998 (pp. 145151). Stroudsburg, PA: Association for Computational Linguistics.
  • Bonnema, R., Bod, R., & Scha, R. (1997). A DOP model for semantic interpretation. In Proceedings ACL 1997 (pp. 159167). Stroudsburg, PA: Association for Computational Linguistics.
  • Borensztajn, G., Zuidema, W., & Bod, R. (2009). Children’s grammars grow more abstract with age — Evidence from an automatic procedure for identifying the productive units of language. Topics in Cognitive Science, 1, 175188.
  • Brown, R. (1973). A first language: The early stages. London: George Allen & Unwin Ltd.
  • Bybee, J. (2006). From usage to grammar: The mind’s response to repetition. Language, 82, 711733.
  • Bybee, J., & Hopper, P. (2001). Frequency and the emergence of linguistic structure. Amsterdam, The Netherlands: John Benjamins.
  • Carroll, J., & Weir, D. (2000). Encoding frequency information in stochastic parsing models. In H.Bunt & A.Nijholt (Eds.), Advances in probabilistic parsing and other parsing technologies (pp. 1328). Dordrecht, The Netherlands: Kluwer.
  • Chater, N. (1999). The search for simplicity: A fundamental cognitive principle? The Quarterly Journal of Experimental Psychology, 52A, 273302.
  • Chi, Z., & Geman, S. (1998). Estimation of probabilistic context-free grammars. Computational Linguistics, 24, 299305.
  • Chiang, D. (2007). Hierarchical phrase-based translation. Computational Linguistics, 33, 201228.
  • Chomsky, N. (1965). Aspects of the theory of syntax. Cambridge, MA: The MIT Press.
  • Chomsky, N. (1966). Cartesian linguistics. New York: Harper & Row.
  • Chomsky, N. (1971). Problems of knowledge and freedom. London: Pantheon Books.
  • Clark, A. (2000). Inducing syntactic categories by context distribution clustering. In Proceedings CoNLL 2000 (pp. 9194). Stroudsburg, PA: Association for Computational Linguistics.
  • Clark, A. (2001). Unsupervised induction of stochastic context-free grammars using distributional clustering. In Proceedings CoNLL 2001 (pp. 105112). Stroudsburg, PA: Association for Computational Linguistics.
  • Clark, A., & Eyraud, R. (2006). Learning auxiliary fronting with grammatical inference. In Proceedings CoNLL 2006 (pp. 3340). Stroudsburg, PA: Association for Computational Linguistics.
  • Collins, M. (1999). Head-driven statistical models for natural language processing. PhD Thesis. Philadelphia: University of Pennsylvania.
  • Collins, M., & Duffy, N. (2002). New ranking algorithms for parsing and tagging: Kernels over discrete structures, and the voted perceptron. In Proceedings ACL 2002 (pp. 99106). Stroudsburg, PA: Association for Computational Linguistics.
  • Conway, C., & Christiansen, N. (2006). Statistical learning within and between modalities. Psychological Science, 17, 905914.
    Direct Link:
  • Crain, S. (1991). Language acquisition in the absence of experience. Behavorial and Brain Sciences, 14, 597612.
  • Crain, S., & Nakayama, M. (1987). Structure dependence in grammar formation. Language, 63, 522543.
  • Croft, B. (2001). Radical construction grammar. Oxford, England: Oxford University Press.
  • Culicover, P., & Nowak, A. (2003). Dynamical grammar. Oxford, England: Oxford University Press.
  • Dennis, S. (2005). An exemplar-based approach to unsupervised parsing. In B.Bara, L.Barsalou, & M.Bucciarell (Eds.), Proceedings CogSci 2005 (pp. 583588). Austin, TX: Cognitive Science Society.
  • Esper, E. (1973). Analogy and association in linguistics and psychology. Atlanta, GA: University of Georgia Press.
  • Fischer, O. (2007). Morphosyntactic change: Functional and formal perspectives. Oxford, England: Oxford University Press (Chapter 3).
  • Freudenthal, D., Pine, J., Aguado-Orea, J., & Gobet, F. (2007). Modelling the developmental patterning of finiteness marking in English, Dutch, German and Spanish using MOSAIC. Cognitive Science, 31, 311341.
  • Gentner, D., & Markman, A. (1997). Structure mapping in analogy and similarity. American Psychologist, 52, 4556.
  • Goldberg, A. (2006). Constructions at work: The nature of generalization in language. Oxford, England: Oxford University Press.
  • Goodman, J. (1996). Efficient algorithms for parsing the DOP model. In Proceedings EMNLP 1996 (pp. 143152). Stroudsburg, PA: Association for Computational Linguistics.
  • Goodman, J. (2003). Efficient parsing of DOP with PCFG-reductions. In R.Bod, R.Scha & K.Sima’an (Eds.), Data-oriented parsing (pp. 125146). Stanford, CA: CSLI Publications.
  • Harris, Z. (1954). Distributional structure. Word, 10, 146162.
  • Hauser, M., Chomsky, N., & Fitch, T. (2002). The faculty of language: What is it, who has it, and how did it evolve? Science, 298, 15691579.
  • Hearne, M., & Way, A. (2004). Data-oriented parsing and the Penn Chinese Treebank. In Proceedings of the first international joint conference natural language processing (pp. 406413). Stroudsburg, PA: Association for Computational Linguistics.
  • Hoogweg, L. (2003). Extending DOP with insertion. In R.Bod, R.Scha & K.Sima’an (Eds.), Data-oriented parsing (pp. 317335). Stanford, CA: CSLI Publications.
  • Huang, L., & Chiang, D. (2005). Better k-best parsing. In Proceedings IWPT 2005 (pp. 5364). Stroudsburg, PA: Association for Computational Linguistics.
  • Itkonen, E. (2005). Analogy as structure and process. Amsterdam, The Netherlands: John Benjamins.
  • Johnson, M. (2002). The DOP estimation method is biased and inconsistent. Computational Linguistics, 28, 7176.
  • Joshi, A. (2004). Starting with complex primitives pays off: Complicate locally, simplify globally. Cognitive Science, 28, 637668.
  • Jurafsky, D. (2003). Probabilistic modeling in psycholinguistics. In R.Bod, J.Hay & S.Jannedy (Eds.), Probabilistic linguistics (pp. 3996). Cambridge, MA: The MIT Press.
  • Kam, X.-N., Stoyneshka, I., Tornyova, L., Fodor, J., & Sakas, W. (2008). Bigrams and the richness of the stimulus. Cognitive Science, 32, 771787.
  • Kaplan, R. (1996). A probabilistic approach to lexical-functional analysis. In Proceedings of the 1996 LFG conference and workshops (pp. 916). Stanford, CA: CSLI Publications.
  • Kay, M. (1980). Algorithmic schemata and data structures in syntactic processing. Report CSL-80-12, Palo Alto, CA: Xerox PARC.
  • Klein, D., & Manning, C. (2002). A general constituent-context model for improved grammar induction. In Proceedings ACL 2002 (pp. 128135). Stroudsburg, PA: Association for Computational Linguistics.
  • Klein, D., & Manning, C. (2004). Corpus-based induction of syntactic structure: Models of dependency and constituency. In Proceedings ACL 2004 (pp. 478485). Stroudsburg, PA: Association for Computational Linguistics.
  • Klein, D., & Manning, C. (2005). Natural language grammar induction with a generative constituent-context model. Pattern Recognition, 38, 14071419.
  • Langacker, R. (1987). Foundations of cognitive grammar: Theoretical prerequisites. Stanford, CA: Stanford University Press.
  • Lewis, J., & Elman, J. (2001). Learnability and the statistical structure of language: Poverty of stimulus arguments revisited. In Proceedings of 26th annual Boston Univ. conference on language development (pp. 359370). Boston: BUCLD.
  • MacWhinney, B. (1978). The acquisition of morphophonology. Monographs of the Society for Research in Child Development 43. Pittsburgh, PA: CMU.
  • MacWhinney, B. (2000). The CHILDES project: Tools for analyzing talk. Mawah, NJ: Erlbaum.
  • MacWhinney, B. (2005). Item-based constructions and the logical problem. In Proceedings of the second workshop on psychocomputational models of human language acquisition, ACL 2005 (pp. 122). Stroudsburg, PA: Association for Computational Linguistics.
  • Manning, C., & Schütze, H. (1999). Foundations of statistical natural language processing. Cambridge, MA: MIT Press.
  • Marcus, M., Santorini, B., & Marcinkiewicz, M. (1993). Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics, 19, 302330.
  • Moerk, E. (1983). The mother of Eve as a first language teacher. Norwood: ABLEX.
  • Neumann, G., & Flickinger, D. (2002). HPSG-DOP: Data-oriented parsing with HPSG. In Proceedings of the ninth international conference on HPSG, HPSG 2002.
  • Perfors, A., Tenenbaum, J., & Regier, T. (2006). Poverty of the stimulus? A rational approach. In R.Sun (Ed.), Proceedings CogSci 2006 (pp. 566561). Austin, TX: Cognitive Science Society.
  • Peters, A. (1983). The units of language acquisition. Cambridge, England: Cambridge University Press.
  • Pinker, S. (1999). Words and rules: The ingredients of language. London: Widenfeld and Nicolson.
  • Pullum, G., & Scholz, B. (2002). Empirical assessment of stimulus poverty arguments. The Linguistic Review, 19, 950.
  • Reali, F., & Christiansen, M. (2005). Uncovering the richness of the stimulus: structure dependence and indirect statistical evidence. Cognitive Science, 29, 10071028.
  • Redington, M., Chater, N., & Finch, S. (1998). Distributional information: A powerful cue for acquiring syntactic categories. Cognitive Science, 22, 425469.
  • Rowland, C. (2007). Explaining errors in children’s questions. Cognition, 104, 106134.
  • Sagae, K., Davis, E., Lavie, A., MacWhinney, B., & Wintner, S. (2007). High-accuracy annotation and parsing of CHILDES transcript. In Proceedings of the workshop on cognitive aspects of computational language acquisition, ACL 2007 (pp. 2532). Stroudsburg, PA: Association for Computational Linguistics.
  • Scha, R. (1990). Taaltheorie en Taaltechnologie; Competence en Performance. In Q.de Kort & G.Leerdam (Eds.), Computertoepassingen in de Neerlandistiek (pp. 722). Almere, The Netherlands: Landelijke Vereniging van Neerlandici.
  • Scha, R., Bod, R., & Sima’an, K. (1999). A memory-based model of syntactic analysis: Data-oriented parsing. Journal of Experimental & Theoretical Artificial Intelligence, 11, 409440.
  • Seginer, Y. (2007). Fast unsupervised incremental parsing. In Proceedings ACL 2007 (pp. 384391). Stroudsburg, PA: Association for Computational Linguistics.
  • Sima’an, K. (1996). Computational complexity of probabilistic disambiguation by means of tree grammars. In Proceedings COLING 1996 (pp. 11751180). Stroudsburg, PA: Association for Computational Linguistics.
  • Skousen, R. (1989). Analogical modeling of language. Dordrecht, The Netherlands: Kluwer.
  • Skut, W., Krenn, B., Brants, T., & Uszkoreit, H. (1997). An annotation scheme for free word order languages. In Proceedings ANLP 1997. Stroudsburg, PA: Association for Computational Linguistics.
  • Solan, D., Horn, D., Ruppin, E., & Edelman, S. (2005). Unsupervised learning of natural languages. Proceedings National Academy of Science, 102, 1162911634.
  • Tomasello, M. (2003). Constructing a language. Cambridge, MA: Harvard University Press.
  • Ullman, S. (2007). Object recognition and segmentation by a fragment-based hierarchy. Trends in Cognitive Sciences, 11, 5864.
  • Xia, F., & Palmer, M. (2001). Converting dependency structures to phrase structures. In Proceedings HLT 2001 (pp. 5460). Stroudsburg, PA: Association for Computational Linguistics.
  • Xue, N., Chiou, F., & Palmer, M. (2002). Building a large-scale annotated Chinese corpus. In Proceedings COLING 2002 (pp. 108115). Stroudsburg, PA: Association for Computational Linguistics.
  • Younger, D. (1967). Recognition and parsing of context-free languages in time n3. Information and Control, 10, 189208.
  • van Zaanen, M. (2000). ABL: Alignment-based learning. In Proceedings COLING 2000 (pp. 961967). Stroudsburg, PA: Association for Computational Linguistics.
  • Zollmann, A., & Sima’an, K. (2005). A consistent and efficient estimator for data-oriented parsing. Journal of Automata, Languages and Combinatorics, 10, 367388.
  • Zuidema, W. (2006). Theoretical evaluation of estimation methods for data-oriented parsing. In Proceedings EACL 2006 (pp. 183186). Stroudsburg, PA: Association for Computational Linguistics.
  • Zuidema, W. (2007). Parsimonious data-oriented parsing. In Proceedings EMNLP 2007 (pp. 551560). Stroudsburg, PA: Association for Computational Linguistics.