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Feature-based generalisation as a source of gradient acceptability*

Published online by Cambridge University Press:  29 June 2009

Adam Albright
Adam Albright
Affiliation:
Massachusetts Institute of Technology
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Abstract

Phonological judgements are often gradient: blick>?bwick>*bnick>**bzick. The mechanisms behind gradient generalisation remain controversial, however. This paper tests the role of phonological features in helping speakers evaluate which novel combinations receive greater lexical support. A model is proposed in which the acceptability of a string is based on the most probable combination of natural classes that it instantiates. The model is tested on its ability to predict acceptability ratings of nonce words, and its predictions are compared against those of models that lack features or economise on feature specifications. The proposed model achieves the best balance of performance on attested and unattested sequences, and is a significant predictor of acceptability even after the other models are factored out. The feature-based model's predictions do not completely subsume those of simpler models, however. This may indicate multiple levels of evaluation, involving segment-based phonotactic probability and feature-based gradient phonological grammaticality.

Type
Articles
Information
Phonology , Volume 26 , Issue 1 , May 2009 , pp. 9 - 41
Copyright
Copyright © Cambridge University Press 2009

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References

REFERENCES

Albright, Adam (2002a). Islands of reliability for regular morphology: evidence from Italian. Lg 78. 684–709.Google Scholar
Albright, Adam (2002b). The lexical bases of morphological well-formedness. In Bendjaballah, Sabrina, Dressler, Wolfgang U., Pfeiffer, Oskar E. & Voeikova, Maria D. (eds.) Morphology 2000. Amsterdam & Philadelphia: Benjamins. 5–15.CrossRefGoogle Scholar
Albright, Adam & Hayes, Bruce (2002). Modeling English past tense intuitions with minimal generalization. In Maxwell, Mike (ed.) Proceedings of the 6th Meeting of the ACL Special Interest Group on Computational Phonology. Philadelphia: Association for Computational Linguistics. 58–69.Google Scholar
Albright, Adam & Hayes, Bruce (2003). Rules vs. analogy in English past tenses: a computational/experimental study. Cognition 90. 119–161.CrossRefGoogle ScholarPubMed
Auer, Edward T. Jr. & Luce, Paul A. (2005). Probabilistic phonotactics in spoken word recognition. In Pisoni, David B. & Remez, Robert E. (eds.) The handbook of speech perception. Malden, Mass. & Oxford: Blackwell. 610–630.CrossRefGoogle Scholar
Baayen, R. H. (2008). Analyzing linguistic data: a practical introduction to statistics using R. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Baayen, R. H., Piepenbrock, Richard & van Rijn, Hedderik (1993). The CELEX lexical data base. [CD-ROM.] Philadelphia: Linguistic Data Consortium, University of Pennsylvania.Google Scholar
Bailey, Todd M. & Hahn, Ulrike (2001). Determinants of wordlikeness: phonotactics or lexical neighborhoods? Journal of Memory and Language 44. 568–591.CrossRefGoogle Scholar
Bates, David, Maechler, Martin & Dai, Bin (2008). The lme4 Package (Version 0.999375-28): linear mixed-effects models using S4 classes. Available (March 2009) at http://cran.r-project.org/web/packages/lme4/lme4.pdf.Google Scholar
Berent, Iris, Steriade, Donca, Lennertz, Tracy & Vaknin, Vered (2007). What we know about what we have never heard: evidence from perceptual illusions. Cognition 104. 591–630.CrossRefGoogle ScholarPubMed
Berko, Jean (1958). The child's learning of English morphology. Word 14. 150–177.CrossRefGoogle Scholar
Berwick, Robert C. (1986). Learning from positive-only examples: the subset principle and three case studies. In Carbonell, Jaime Guillermo, Michalski, Ryszard S. & Mitchell, Tom M. (eds.) Machine learning: an artificial intelligence approach. Vol. 2. Los Altos, Ca.: Kaufmann. 625–645.Google Scholar
Buchwald, Adam (2007). Determining well-formedness in phonology: type vs. token frequency. Paper presented at the 81st Annual Meeting of the Linguistic Society of America, Anaheim.Google Scholar
Bybee, Joan (1995). Regular morphology and the lexicon. Language and Cognitive Processes 10. 425–455.CrossRefGoogle Scholar
Chomsky, Noam & Halle, Morris (1968). The sound pattern of English. New York: Harper & Row.Google Scholar
Clements, G. N. & Keyser, Samuel J. (1983). CV phonology: a generative theory of the syllable. Cambridge, Mass.: MIT Press.Google Scholar
Coleman, John & Pierrehumbert, Janet (1997). Stochastic phonological grammars and acceptability. In Coleman, John (ed.) Proceedings of the 3rd Meeting of the ACL Special Interest Group in Computational Phonology. Somerset, NJ: Association for Computational Linguistics. 49–56.Google Scholar
Coltheart, Max, Davelaar, Eileen, Jonasson, Jon Torfi & Besner, Derek (1977). Access to the internal lexicon. In Dornic, Stan (ed.) Attention and performance. Vol. 6. Hillsdale, NJ: Erlbaum. 535–555.Google Scholar
Davis, Stuart (1984). Some implications of onset-coda constraints for syllable phonology. CLS 20. 46–51.Google Scholar
Frisch, Stefan A., Large, Nathan R. & Pisoni, David B. (2000). Perception of wordlikeness: effects of segment probability and length on the processing of nonwords. Journal of Memory and Language 42. 481–496.CrossRefGoogle ScholarPubMed
Greenberg, Joseph H. & Jenkins, James J. (1964). Studies in the psychological correlates of the sound system of American English. Word 20. 157–177.CrossRefGoogle Scholar
Hammond, Michael (1999). The phonology of English: a prosodic optimality-theoretic approach. Oxford: Oxford University Press.Google Scholar
Hay, Jennifer (2003). Causes and consequences of word structure. New York & London: Routledge.Google Scholar
Hay, Jennifer, Pierrehumbert, Janet & Beckman, Mary E. (2004). Speech perception, well-formedness and the statistics of the lexicon. In Local, John, Ogden, Richard & Temple, Rosalind (eds.) Phonetic interpretation: papers in laboratory phonology VI. Cambridge: Cambridge University Press. 58–74.Google Scholar
Hayes, Bruce (2004). Phonological acquisition in Optimality Theory: the early stages. In Kager et al. (2004). 158–203.CrossRefGoogle Scholar
Hayes, Bruce & Wilson, Colin (2008). A maximum entropy model of phonotactics and phonotactic learning. LI 39. 379–440.Google Scholar
Jurafsky, Daniel & Martin, James H. (2000). Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition. Upper Saddle River, NJ: Prentice Hall.Google Scholar
Jusczyk, Peter W., Luce, Paul A. & Charles-Luce, Jan (1994). Infants' sensitivity to phonotactic patterns in the native language. Journal of Memory and Language 33. 630–645.CrossRefGoogle Scholar
Kager, René, Pater, Joe & Zonneveld, Wim (eds.) (2004). Constraints in phonological acquisition. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Kendall, Maurice G. (1990). Rank correlation methods. 5th edn. New York: Oxford University Press.Google Scholar
Kessler, Brett & Treiman, Rebecca (1997). Syllable structure and the distribution of phonemes in English syllables. Journal of Memory and Language 37. 295–311.CrossRefGoogle Scholar
Luce, Paul A. (1986). Neighborhoods of words in the mental lexicon. Technical report, Speech Research Laboratory, Department of Psychology, Indiana University.Google Scholar
Luce, Paul A. & Pisoni, David B. (1998). Recognizing spoken words: the neighborhood activation model. Ear and Hearing 19. 1–36.CrossRefGoogle ScholarPubMed
Newman, Rochelle S., Sawusch, James R. & Luce, Paul A. (1997). Lexical neighborhood effects in phonetic processing. Journal of Experimental Psychology: Human Perception and Performance 23. 873–889.Google ScholarPubMed
Nosofsky, Robert M. (1986). Attention, similarity, and the identification–categorization relationship. Journal of Experimental Psychology: General 115. 39–57.CrossRefGoogle ScholarPubMed
Ohala, John J. & Ohala, Manjari (1986). Testing hypotheses regarding the psychological manifestation of morpheme structure constraints. In Ohala, John J. & Jaeger, Jeri (eds.) Experimental phonology. Orlando: Academic Press. 239–252.Google Scholar
Pierrehumbert, Janet B. (2001). Exemplar dynamics: word frequency, lenition and contrast. In Bybee, Joan & Hopper, Paul (eds.) Frequency and the emergence of linguistic structure. Amsterdam & Philadelphia: Benjamins. 137–157.CrossRefGoogle Scholar
Pinheiro, José C. & Bates, Douglas M. (2000). Mixed-effects models in S and S-PLUS. New York: Springer.CrossRefGoogle Scholar
Pitt, Mark A. & McQueen, James M. (1998). Is compensation for coarticulation mediated by the lexicon? Journal of Memory and Language 39. 347–370.CrossRefGoogle Scholar
Prasada, Sandeep & Pinker, Steven (1993). Generalization of regular and irregular morphological patterns. Language and Cognitive Processes 8. 1–56.CrossRefGoogle Scholar
Prince, Alan & Smolensky, Paul (2004). Optimality Theory: constraint interaction in generative grammar. Malden, Mass. & Oxford: Blackwell.CrossRefGoogle Scholar
Prince, Alan & Tesar, Bruce (2004). Learning phonotactic distributions. In Kager, et al. (2004). 245–291.Google Scholar
R Development Core Team (2008). R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. Available (March 2009) at http://www.r-project.org.Google Scholar
Ramscar, Michael & Yarlett, Daniel (2003). Semantic grounding in models of analogy: an environmental approach. Cognitive Science 27. 41–71.CrossRefGoogle Scholar
Saul, Lawrence & Pereira, Fernando (1997). Aggregate and mixed-order Markov models for statistical language processing. In Cardie, Claire & Weischedel, Ralph (eds.) Proceedings of the 2nd Conference on Empirical Methods in Natural Language Processing. Somerset, NJ: Association for Computational Linguistics. 81–89.Google Scholar
Scholes, Robert J. (1966). Phonotactic grammaticality. The Hague: Mouton.CrossRefGoogle Scholar
Schütze, Carson T. (2005). Thinking about what we are asking speakers to do. In Kepser, Stephan & Reis, Marga (eds.) Linguistic evidence: empirical, theoretical and computational perspectives. Berlin & New York: Mouton de Gruyter. 457–484.CrossRefGoogle Scholar
Selkirk, Elizabeth O. (1982). The syllable. In Hulst, Harry van der & Smith, Norval (eds.) The structure of phonological representations. Part 2. Dordrecht: Foris. 337–384.Google Scholar
Sendlmeier, Walter F. (1987). Auditive judgements of word similarity. Zeitschrift für Phonetik, Sprachwissenschaft und Kommunikationsforschung 40. 538–547.Google Scholar
Shademan, Shabnam (2006). Is phonotactic knowledge grammatical knowledge? WCCFL 25. 371–379.Google Scholar
Shademan, Shabnam (2007). Grammar and analogy in phonotactic well-formedness judgments. PhD thesis, University of California, Los Angeles.Google Scholar
Sheskin, David J. (2004). Handbook of parametric and nonparametric statistical procedures. 3rd edn. Boca Raton: Chapman & Hall.Google Scholar
Tessier, Anne-Michelle (2006). Biases and stages in phonological acquisition. PhD dissertation, University of Massachusetts, Amherst.Google Scholar
Vitevitch, Michael S. & Luce, Paul A. (1998). When words compete: levels of processing in perception of spoken words. Psychological Science 9. 325–329.CrossRefGoogle Scholar
Vitevitch, Michael S. & Luce, Paul A. (1999). Probabilistic phonotactics and neighborhood activation in spoken word recognition. Journal of Memory and Language 40. 374–408.CrossRefGoogle Scholar
Vitevitch, Michael S. & Luce, Paul A. (2004). A web-based interface to calculate phonotactic probability for words and nonwords in English. Behavior Research Methods, Instruments, and Computers 36. 481–487.CrossRefGoogle ScholarPubMed
Vitevitch, Michael S. & Luce, Paul A. (2005). Increases in phonotactic probability facilitate spoken nonword repetition. Journal of Memory and Language 52. 193–204.CrossRefGoogle Scholar
Vitevitch, Michael S., Luce, Paul A., Charles-Luce, Jan & Kemmerer, David (1996). Phonotactic and metrical influences on adult ratings of spoken nonsense words. Proceedings of the International Conference on Spoken Language Processing (ICSLP). 82–85.Google Scholar
Vitevitch, Michael S., Luce, Paul A., Charles-Luce, Jan & Kemmerer, David (1997). Phonotactics and syllable stress: implications for the processing of spoken nonsense words. Language and Speech 40. 47–62.CrossRefGoogle ScholarPubMed