416,211 research outputs found
Computational and Robotic Models of Early Language Development: A Review
We review computational and robotics models of early language learning and
development. We first explain why and how these models are used to understand
better how children learn language. We argue that they provide concrete
theories of language learning as a complex dynamic system, complementing
traditional methods in psychology and linguistics. We review different modeling
formalisms, grounded in techniques from machine learning and artificial
intelligence such as Bayesian and neural network approaches. We then discuss
their role in understanding several key mechanisms of language development:
cross-situational statistical learning, embodiment, situated social
interaction, intrinsically motivated learning, and cultural evolution. We
conclude by discussing future challenges for research, including modeling of
large-scale empirical data about language acquisition in real-world
environments.
Keywords: Early language learning, Computational and robotic models, machine
learning, development, embodiment, social interaction, intrinsic motivation,
self-organization, dynamical systems, complexity.Comment: to appear in International Handbook on Language Development, ed. J.
Horst and J. von Koss Torkildsen, Routledg
Non-adjacent dependency learning in infancy, and its link to language development
To acquire language, infants must learn how to identify words and linguistic structure in speech. Statistical learning has been suggested to assist both of these tasks. However, infantsâ capacity to use statistics to discover words and structure together remains unclear. Further, it is not yet known how infantsâ statistical learning ability relates to their language development. We trained 17-month-old infants on an artificial language comprising non-adjacent dependencies, and examined their looking times on tasks assessing sensitivity to words and structure using an eye-tracked head-turn-preference paradigm. We measured infantsâ vocabulary size using a Communicative Development Inventory (CDI) concurrently and at 19, 21, 24, 25, 27, and 30 months to relate performance to language development. Infants could segment the words from speech, demonstrated by a significant difference in looking times to words versus part-words. Infantsâ segmentation performance was significantly related to their vocabulary size (receptive and expressive) both currently, and over time (receptive until 24 months, expressive until 30 months), but was not related to the rate of vocabulary growth. The data also suggest infants may have developed sensitivity to generalised structure, indicating similar statistical learning mechanisms may contribute to the discovery of words and structure in speech, but this was not related to vocabulary size
Using R, LaTeX and Wiki for an Arabic e-learning platform
E-learning plays an important role in education as it supports online education via computer networks and provides educational services by utilising information technologies. We present a case study describing the development of an Arabic language elearning course in statistics. Discussed are issues concerning e-learning in Arab countries with special focus on problems of the application of e-learning in the Arab world and the difficulties concerning the design Arabic platforms such as language problems, cultural and technical problems, especially ArabTeX works difficulty with LaTeX format. Thus Wiki is offered as a solution to such problems. Wiki supports LaTeX and other statistical programs, for instance R, andWiki offers the solution to language problems. Details of this technology are discussed and a solution as to how this system can serve in building an Arab platform is presented.E-learning, MM*Stat, Wiki, ArabTeX, Statistical software
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