Abstract
If we can predict the words a child is likely to learn next, it may lay the foundations for developing a tool to assist child language acquisition, especially for children experiencing language delay. Previous studies have demonstrated vocabulary predictions using neural network techniques and graph models; however, individually these models do not fully capture the complexities of language learning in infants. In this paper, we describe a multi-relationship-layer predictive model, based on a graph neural network. Our model combines vocabulary development over time with quantified connections between words calculated from fifteen different norms, incorporating an ensemble output stage to combine the predictions from each layer. We present results from each relationship layer and the most effective ensemble arrangement.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Feinstein, L., Duckworth, K.: Development in the early years: its importance for school performance and adult outcomes. Centre for Research on the Wider Benefits of Learning, London (2006)
Scerri, T.S., et al.: DCDC2, KIAA0319 and CMIP are associated with reading-related traits. Biol Psychiatry 70(3), 237–245 (2011)
Lindsay, G., et al.: Educational provision for children with specific speech and language difficulties in England and Wales. In: IoE and CEDAR (2002)
Clegg, J., et al.: Developmental language disorders - a follow-up in later adult life. cognitive, language and psychosocial outcomes. J. Child Psychol. Psychiat. 46, 128–149 (2005)
Roulstone, S., et al.: Investigating the role of language in children’s early educational outcomes. Technical Report DFE-RR134, Department of Education, UK (2011)
Fenson, L., et al.: MacArthur-Bates Communicative Development Inventories. Paul H. Brookes Publishing Company, Baltimore (2007)
Alcock, K.J., et al.: Construction and standardisation of the UK communicative development inventory (UK-CDI), words and gestures. In: International Conference on Infant Studies (2016)
Stadthagen-Gonzalez, H., Davis, C.J.: The Bristol norms for age of acquisition, imageability, and familiarity. Behav. Res. Methods 38(4), 598–605 (2006)
Johnson, E.K., Jusczyk, P.W.: Word segmentation by 8-month-olds: when speech cues count more than statistics. J. Mem. Lang. 44, 548–567 (2001)
Beckage, N., Mozer, M., Colunga, E.: Predicting a child’s trajectory of lexical acquisition. In: Noelle, D.C., et al. (eds.) 37th Annual Meeting of the Cognitive Science Society, CogSci. cognitivesciencesociety.org (2015)
Beckage, N.M., Mozer, M.C., Colunga, E.: Quantifying the role of vocabulary knowledge in predicting future word learning. IEEE Trans. Cogn. Dev. Syst. 12, 148–159 (2020)
McRae, K., et al.: Semantic feature production norms for a large set of living and nonliving things. Behav. Res. Methods 37, 547–559 (2005)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Bengio, Y., LeCun, Y. (eds.) 1st International Conference on Learning Representations, ICLR 2013, Workshop Track Proceedings (2013)
Ke, J., Yao, Y.: Analysing language development from a network approach. J. Quant. Linguist. 15(1), 70–99 (2008)
Sims, C., Schilling, S., Colunga, E.: Exploring the developmental feedback loop: word learning in neural networks and toddlers. In: Knauff, M., Pauen, M., Sebanz, N., Wachsmuth, I. (eds.) Proceedings of the Annual Meeting of the Cognitive Science Society, CogSci 2013, vol. 35, pp. 3408–3413 (2013)
Nelson, D.L., et al.: The university of South Florida free association, rhyme, and word fragment norms. Behav. Res. Methods Inst. Comput. 36, 402–407 (2004)
Buchanan, L., Westbury, C., Burgess, C.: Characterizing semantic space: neighborhood effects in word recognition. Psychon. Bull. Rev. 8, 531–544 (2001)
Robinson, T.: British English Example Pronunciation (BEEP) dictionary (1996). http://svr-www.eng.cam.ac.uk/comp.speech/Section1/Lexical/beep.html
Lynott, D., et al.: The Lancaster Sensorimotor Norms: multidimensional measures of perceptual and action strength for 40,000 English words. Behav. Res. Methods 52(3), 1271–1291 (2020)
Gori, M., Monfardini, G., Scarselli, F.: A new model for learning in graph domains. In: Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005, Montreal, Que, Canada, vol. 2, pp. 729–734. IEEE (2005)
Bruna, J., et al.: Spectral networks and locally connected networks on graphs. arXiv:1312.6203 [cs] (2014)
Kipf, T., Welling, M.: Semi-supervised classification with graph convolutional networks. In: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, 24–26 April 2017, Conference Track Proceedings (2017)
Zhao, L., et al.: T-GCN: a temporal graph convolutional network for traffic prediction. IEEE Trans. Intell. Transp. Syst. 21(9), 3848–3858 (2020)
Jiang, W., Luo, J.: Graph neural network for traffic forecasting: a survey. Expert Syst. Appl., 117921 (2022)
CSIRO’s Data61. Stellargraph machine learning library (2018). https://github.com/stellargraph/stellargraph
Abadi, M., et al.: Tensorflow: large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/
Frank, M.C., Braginsky, M., Yurovsky, D., et al.: Wordbank: an open repository for developmental vocabulary data. J. Child Lang. 44, 677–694 (2017)
Zhang, C., Ma, Y. (eds.): Ensemble Machine Learning. Springer, Heidelberg (2012). https://doi.org/10.1007/978-1-4419-9326-7
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Roxburgh, A., Grasso, F., Payne, T.R. (2023). A Multi-relationship Language Acquisition Model for Predicting Child Vocabulary Growth. In: Iliadis, L., Maglogiannis, I., Alonso, S., Jayne, C., Pimenidis, E. (eds) Engineering Applications of Neural Networks. EANN 2023. Communications in Computer and Information Science, vol 1826. Springer, Cham. https://doi.org/10.1007/978-3-031-34204-2_14
Download citation
DOI: https://doi.org/10.1007/978-3-031-34204-2_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34203-5
Online ISBN: 978-3-031-34204-2
eBook Packages: Computer ScienceComputer Science (R0)