Learning Aspect in Italian L2: corpus annotation, acquisitional patterns, and connectionist modelling

Within acquisitional studies, the learning of Aspect represents one of the most intriguing and debated issues. The present book provides an overview of this long tradition of research and addresses the emergence of Aspect in Italian L2 using corpus compilation and annotation, data analysis and computational simulations. Given the increasing interest in annotated language resources, a specific schema is proposed for the annotation of contextual information connected with the aspectual features of predicates in a learner corpus. The qualitative and quantitative analyses focus on the interplay between Aspect, Actionality and Grounding in longitudinal Italian L2 data elicited from Spanish-speaking and German-speaking learners, in order to gain insight into the role of L1 and of factors such as saliency and frequency in acquisitional patterns. Moreover, learner development is compared with results of experiments carried out by unsupervised neural networks, which offer an innovative device to model the learning processes and the internal dynamics of linguistic systems. The convergence between computational simulations and learners patterns gives evidence for the cooperation between data-driven mechanisms and cognitive principles in such a complex process as second language acquisition. This book, accompanied by a summary in Italian, will be of interest to applied linguists, researchers in second language acquisition, learner corpora and computational linguistics.