Many theorists have dismissed a priori the idea that distributional information could play a significant role in syntactic category acquisition. We demonstrate empirically that such information provides a powerful cue to syntactic category membership, which can be exploited by a variety of simple, psychologically plausible mechanisms. We present a range of results using a large corpus of child-directed speech and explore their psychological implications. While our results show that a considerable amount of information concerning the syntac-tic categories can be obtained from distributional information alone, we stress that many other sources of information may also be potential contributors to the identification of syntactic classes. I.

Current-generation speech recognition systems seek to identify words via analysis of their underlying phonological constituents. Although this stratagem works well for carefully enunciated speech emanating from a pristine acoustic environment, it has fared less well for recognizing speech spoken under more realistic conditions, such as (1) moderate to high levels of background noise (2) moderately reverberant acoustic environments (3) spontaneous, informal conversation Under such "real-world" conditions the acoustic properties of speech make it difficult to partition the acoustic stream into readily definable phonological units, thus rendering the process of word recognition highly vulnerable to departures from "canonical" patterns. Analysis of informal, spontaneous speech indicates that the stability of linguistic representation is more likely to reside on the syllabic and phrasal levels than on the phonological. In consequence, attempts to represent words merely as sequences of ...

Maternal infant-directed speech in Mandarin Chinese and Turkish (two mother–child dyads each; ages of children between 0;11 and 1;8) was examined to see if cues exist in input that might assist infants’ assignment of words to lexical and functional item categories. Distributional, phonological, and acoustic measures were analysed. In each language, lexical and functional items (i.e. syllabic morphemes) differed significantly on numerous measures. Despite differences in mean values between categories, distributions of values typically displayed substantial overlap. However, simulations with self-organizing neural networks supported the conclusion that although individual dimensions had low cue validity, in each language multidimensional constellations of presyntactic cues are sufficient to guide assignment of words to rudimentary grammatical categories.

Automatic speech recognition in the twenty-first century will strive to emulate many properties of human speech understanding that currently lie beyond the capability of present-day systems. Such future-generation recognition will require massive amounts of empirical data in order to derive the organizational principles underlying the generation and decoding of spoken language. Such data can be efficiently collected through systematic computational experimentation designed to identify the important building blocks of speech and delineate the nature of the structural interactions among linguistic tiers associated with the extraction of semantic information.

Recent work in developmental psycholinguistics suggests that children may bootstrap grammatical categories and basic syntactic structure by exploiting distributional, phonological, and prosodic cues. Previous connectionist work has indicated that multiple-cue integration is computationally feasible for small artificial languages. In this paper, we present a series of simulations exploring the integration of distributional and phonological cues in a connectionist model trained on a fullblown corpus of child-directed speech. In the first simulation, we demonstrate that the connectionist model performs very well when trained on purely distributional information represented in terms of lexical categories. In the second simulation we demonstrate that networks trained on distributed vectors incorporating phonetic information about words also achieve a high level of performance. Finally, we employ discriminant analyses of hidden unit activations to show that the networks are able to integrate phonological and distributional cues in the service of developing highly reliable internal representations of lexical categories.

Before children can ride a bicycle or tie their shoes, they have learned a great deal about how words are combined to form complex sentences. This achievement is especially impressive because children acquire most of this syntactic knowledge with little or no direct instruction. Nevertheless, mastering natural language syntax may be among the most difficult learning tasks

The goals of the present study were: (1) to determine whether grammatical gender on a noun modifier can prime recognition of the following noun; (2) to determine whether the priming effect involves facilitation, inhibition or both, and (3) to compare performance across three different tasks that vary in the degree to which explicit attention to gender is required, including word repetition, gender monitoring and grammaticality judgment. Results showed a clear effect of gender priming, involving both facilitation and inhibition. Priming was observed whether or not the subjects' attention was directed to gender per se. Results suggest that gender priming involves a combination of controlled, post-lexical processing and automatic, pre-lexical processing. Implications for different models of lexical access are discussed, with special reference to modular vs. interactive-activation theories. Why grammatical priming? The issue of whether gender can be a useful prime in lexical access is a ...

Traditional linguistic models are categorical. Recently, though, a number of researchers have begun to study non-categorical human linguistic knowledge (e.g. Bender 2000, Pierrehumbert 2000, Frisch 2001). This new empirical focus has posed significant difficulties for categorical models, which cannot account for many non-categorical phenomena. Rather than trying to fit the non-categorical complexities of language into categorical models, a number of researchers have begun to treat non-categoriality in probabilistic terms (Jurafsky 1996, Abney 1996, Bod 1998). This dissertation demonstrates experimentally that language users have knowledge of non-categorical correlations between phonology and other grammatical, semantic, and social knowledge and that they apply this knowledge to the task of language perception. The thesis also proposes neural explanations for the behavior exhibited in the experiments, and develops neurally plausible, probabilistic computational models to this end. This first half of this dissertation presents new evidence of the non-categoriality of human linguistic knowledge through two case studies. The first addresses the relation between sound and meaning, though an experimental investigation of the psychological reality of English phonaesthemes, and shows that these non-categorical sub-morphemic sound-meaning pairings are psychologically real. A second, larger study addresses the multiple factors that non-categorically affect a particular morpho-phonological process in French, called liaison. These two studies provide evidence that language users access noncategorical relations between phonological patterns and their phonological, morphological, syntactic, semantic, and social correlates. An additional result of the liaison study is the finding that language users exhibit unconscious knowledge of non-categorical interactions between factors that influence this morpho-phonological process. While there are general neural explanations for the ability to learn and represent the knowledge suggested by these studies, a formal model can only be produced in a computational architecture. Therefore, in the dissertation's second half, I develop a computational model of non-categorical, cross-modal knowledge using a probabilistic architecture used in Artificial Intelligence research, known as Belief Networks (Pearl 1988). In addition to capturing the generalizations about non-categorical knowledge evidenced by the two case studies, Belief Networks are neurally plausible, making them a sound architecture for a bridging model between neural structure and cognitive and linguistic behavior.

Syntactic priming of Chinese nouns and verbs was investigated, in word recognition (cued shadowing of auditory targets) and production (picture naming). Disyllabic compound words were presented after syntactically congruent, incongruent or neutral auditory contexts, with a zero delay between offset of the context and onset of the target. Significant priming was observed in both tasks, including facilitation as well as inhibition. Post hoc analyses showed that reaction times were also affected by sublexical variables that are especially relevant for Chinese, including syllable density (number of word types and tokens in the language with the same first or second syllable) and semantic transparency (whether the meaning of the whole word is predictable from the separate meanings of the two syllables within the compound). These patterns suggest competitive effects at the sublexical level. Implications for interactive models of lexical access are discussed. 1

We propose an explicit, incremental strategy by which children could group words with similar syntactic privileges into discrete, unlabeled categories. This strategy, which can discover lexical ambiguity, is based in part on a generalization of the idea of sentential minimal pairs. As a result, it makes minimal assumptions about the availability of syntactic knowledge at the onset of categorization. Although the proposed strategy is distributional, it can make use of categorization cues from other domains, including semantics and phonology. Computer simulations show that this strategy is effective at categorizing words in both artificial-language samples and transcripts of naturally-occurring, child-directed speech. Further, the simulations show that the proposed strategy performs even better when supplied with semantic information about concrete nouns. Implications for theories of categorization are discussed.