A Connectionist account of Spanish determiner production (original) (raw)
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A connectionist model of Spanish determiner production
2003
Evidence from experimental studies of Spanish children's production of determiners reveals that they pay more attention to phonological cues present in nouns than to natural semantics when assigning gender to determiners (Pérez-Pereira, 1991). This experimental work also demonstrated that Spanish children are more likely to produce the correct determiner when given a noun with phonological cues which suggest it is masculine, and more likely to assign masculine gender to nouns with ambiguous cues. In this paper, we investigate the phonological cues available to children and seek to explore the possibility that differential frequency in the linguistic input explains the priority given to masculine forms when children are faced with ambiguous novel items. A connectionist model of determiner production was incrementally trained on a lexicon of determiner-noun phrases taken from parental speech in [*
Connectionist Modeling for… er… linguists
2001
Connectionist modeling (AKA neural network modeling, connectionism) is rapidly becoming a dominant descriptive and theoretical tool for the psycholinguist. Below is a brief introduction to some of the terms and concepts used in connectionist modeling. Connectionist models are no different than any other sorts of theories in cognitive science, they merely offer a new computational toolbox, or set of algorithmic constraints on models and theories of cognitive phenomena. In this paper I review many of the important components of connectionist models and introduce some of strengths, pitfalls and caveats that casual readers and serious modelers must be aware of.
A Connectionist Model of English Past Tense and Plural Morphology
Cognitive Science, 1999
The acquisition of English noun and verb morphology is modeled using a single-system connectionist network. The network is trained to produce the plurals and past tense forms of a large corpus of monosyllabic English nouns and verbs. The developmental trajectory of network performance is analyzed in detail and is shown to mimic a number of important features of the acquisition of English noun and verb morphology in young children. These include an initial error-free period of performance on both nouns and verbs followed by a period of intermittent over-regularization of irregular nouns and verbs. Errors in the model show evidence of phonological conditioning and frequency effects. Furthermore, the network demonstrates a strong tendency to regularize denominal verbs and deverbal nouns and masters the principles of voicing assimilation. Despite their incorporation into a singlesystem network, nouns and verbs exhibit some important differences in their profiles of acquisition. Most importantly, noun inflections are acquired earlier than verb inflections. The simulations generate several empirical predictions that can be used to evaluate further the suitability of this type of cognitive architecture in the domain of inflectional morphology.
Connectionist Learning of Natural Language Lexical Phonotactics
1998
Abstract Connectionist learning of natural language words and their phonetic regularities is presented. The Neural Network (NN) model we employ in this problem is the Simple Recurrent Network, trained with the Backpropagation Through Time (BPTT) learning algorithm. During the training, it was assigned the task of predicting the next phoneme given one phoneme at each moment and keeping information of the past phonemes from a given word in a few context neurons.
In traditional models of language production grammatical categories are represented as abstract features independent of semantics and phonology. An alternative view is proposed where syntactic categories emerge as a higher-order regularity from semantic and phonological properties of words. The proposal was tested using grammatical gender in Serbian, a south Slavic language with rich morphology. Semantic and phonological correlates of gender are described using a corpus of 1221 Serbian nouns. A PDP network was trained to produce the same words based on distributed semantic representation as input and distributed phonological representation as output, and with no explicit representation of grammatical gender. Upon successful learning of the training corpus, generalization was explored using test corpora designed to capture semantic and phonological properties of different genders. The findings suggest that grammatical gender, as other syntactic categories, may be viewed as emerging through coherent co-variation of semantic and phonological properties of words during learning.
Journal of Memory and Language, 2002
The role of the phonological word as a planning unit in the production of noun phrases (NPs) was investigated in three picture-word interference experiments. We addressed this issue cross-linguistically by asking Spanish and English speakers to produce simple (determiner ϩ noun [in English]) and complex (determiner ϩ adjective ϩ noun [in English] or determiner ϩ noun ϩ adjective [in Spanish]) NPs while ignoring phonologically related or unrelated distractors. The results showed that naming latencies are faster when the distractor is phonologically related to the noun or to the adjective irrespective of the type of NP tested. The results suggest that NP naming latencies are affected by the level of activation of the phonological content of the lexical nodes of the NP, regardless of whether they belong to the first or second phonological word. The results are interpreted in the framework of theories of phonological encoding.