istic methods for syntax, just as for a long time McCarthy and Hayes (1969) discouraged exploration of probabilistic methods in Artificial Intelligence. Among his arguments were that: (i) Probabilistic models wrongly mix in world knowledge (New York occurs more in text than Dayton, Ohio, but for no linguistic reason), (ii) Probabilistic models don't model grammaticality (neither Colorless green ideas sleep furiously nor Furiously sleep ideas green colorless have previously been uttered -- and hence must be estimated to have probability zero, Chomsky wrongly assumes -- but the former is grammatical while the latter is not, and (iii) Use of probabilities does not meet the goal of describing the mind-internal I-language as opposed to the observed-in-the-world E-language. This chapter is not meant to be a detailed critique of Chomsky's arguments -- Abney (1996) provides a survey and a rebuttal, and Pereira (2000) has further useful discussion -- but some of these concerns are still importa

There is an active debate within the field of phonology concerning the cognitive status of substantive phonetic factors such as ease of articulation and perceptual distinctiveness. A new framework is proposed in which substance acts as a bias, or prior, on phonological learning. Two experiments tested this framework with a method in which participants are first provided highly impoverished evidence of a new phonological pattern, and then tested on how they extend this pattern to novel contexts and novel sounds. Participants were found to generalize velar palatalization (e.g., the change from [k]asinkeep to [t�ʃ]asincheap) in a way that accords with linguistic typology, and that is predicted by a cognitive bias in favor of changes that relate perceptually similar sounds. Velar palatalization was extended from the mid front vowel context (i.e., before [e]asincape) to the high front vowel context (i.e., before [i]asin keep), but not vice versa. The key explanatory notion of perceptual similarity is quantified with a psychological model of categorization, and the substantively biased framework is formalized as a conditional random field. Implications of these results for the debate on substance, theories of phonological generalization, and the formalization of similarity are discussed.

Optimality Theory (OT) is a model of language that combines aspects of generative and connectionist linguistics. It is unique in the field in its use of a rank ordering on constraints, which is used to formalize optimization, the choice of the best of a set of potential linguistic forms. We show that phenomena argued to require ranking fall out equally from the form of optimization in OT’s predecessor Harmonic Grammar (HG), which uses numerical weights to encode the relative strength of constraints. We further argue that the known problems for HG can be resolved by adopting assumptions about the nature of constraints that have precedents both in OT and elsewhere in computational and generative linguistics. This leads to a formal proof that if the range of each constraint is a bounded number of violations, HG generates a finite number of languages. This is nontrivial, since the set of possible weights for each constraint is nondenumerably infinite. We also briefly review some advantages of HG. 1

Abstract not found

Recent years have seen a heightened interest in incorporating a wide range of nonphonological factors, including phonetic, morphological, lexical, syntactic, social, and usage factors, into explaining

This series consists of unpublished “working” papers. They are not final versions and may be superseded by publication in journal or book form, which should be cited in preference. All rights remain with the author(s) at this stage, and circulation of a work in progress in this series does not prejudice its later publication. Comments to authors are welcome.

We describe models of Mandarin prosody that allow us to make quantitative measurements of prosodic strengths. These models use Stem-ML, which is a phenomenological model of the muscle dynamics and planning process that controls the tension of the vocal folds, and therefore the pitch of speech. Because Stem-ML describes the interactions between nearby tones, we were able to capture surface tonal variations using a highly constrained model with only one template for each lexical tone category, and a single prosodic strength per word. The model accurately reproduces the intonation of the speaker, capturing 87 % of the variance of fo with these strength parameters. The result reveals alternating metrical patterns in words, and shows that the speaker marks a hierarchy of boundaries by controlling the prosodic strength of words. The strengths we obtain are also correlated with syllable duration, mutual information and part-of-speech.

This series consists of unpublished “working” papers. They are not final versions and may be superseded by publication in journal or book form, which should be cited in preference. All rights remain with the author(s) at this stage, and circulation of a work in progress in this series does not prejudice its later publication. Comments to authors are welcome.

Processes traditionally described as assimilations fall into two main types according to the impact they have on the informational content of speech signals. In grammar-internal assimilation, exemplified by vowel harmony, sound properties that are suprasegmentally extended have a linguistic marking function. In grammar-external assimilation, exemplified by lenition, the extended properties belong to the carrier signal.