Computational investigation of early child language acquisition using multimodal neural networks: a review of three models Export

@article{citeulike:6003963, abstract = {Abstract\ \ Current opinion suggests that language is a cognitive process in which different modalities such as perceptual entities, communicative intentions and speech are inextricably linked. As such, the process of child language acquisition is one in which the child learns to decipher this inextricability and to acquire language capabilities starting from gesturing, followed by language dominated by single word utterances, through to full-blown native language capability. In this paper I review three multimodal neural network models of early child language acquisition. Using these models, I show how computational modelling, in conjunction with the availability of empirical data, can contribute towards our understanding of child language acquisition. I conclude this paper by proposing a control theoretic approach towards modelling child language acquisition using neural networks.}, author = {Nyamapfene, Abel}, citeulike-article-id = {6003963}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/s10462-009-9125-6}, citeulike-linkout-1 = {http://www.springerlink.com/content/d341577138885874}, doi = {10.1007/s10462-009-9125-6}, journal = {Artificial Intelligence Review}, keywords = {language\_acquisition, modeling, neural\_network\_models, review}, posted-at = {2009-10-25 02:58:23}, priority = {4}, title = {Computational investigation of early child language acquisition using multimodal neural networks: a review of three models}, url = {http://dx.doi.org/10.1007/s10462-009-9125-6} }

TY - JOUR ID - citeulike:6003963 L3 - citeulike-article-id:6003963 N2 - Abstract  Current opinion suggests that language is a cognitive process in which different modalities such as perceptual entities, communicative intentions and speech are inextricably linked. As such, the process of child language acquisition is one in which the child learns to decipher this inextricability and to acquire language capabilities starting from gesturing, followed by language dominated by single word utterances, through to full-blown native language capability. In this paper I review three multimodal neural network models of early child language acquisition. Using these models, I show how computational modelling, in conjunction with the availability of empirical data, can contribute towards our understanding of child language acquisition. I conclude this paper by proposing a control theoretic approach towards modelling child language acquisition using neural networks. JF - Artificial Intelligence Review TI - Computational investigation of early child language acquisition using multimodal neural networks: a review of three models KW - language_acquisition KW - modeling KW - neural_network_models KW - review AU - Nyamapfene, Abel UR - http://dx.doi.org/10.1007/s10462-009-9125-6 ER -