ISACCO: a corpus for investigating spoken and written language development in Italian school--age children
Authors: Dominique Brunato, Felice Dell'Orletta (CNR-ILC, 2015)
Topic: Learner Corpora and Language Acquisition

Abstract
We present ISACCO (Italian school–age children corpus) , a new corpus of oral and written retellings of Italianspeaking children attending the primary school. All texts were digitalized and automatically enriched with linguistic information allowing preliminary explorations based on NLP features. Written retellings were also manually annotated with a typology of linguistic errors. The resource is conceived to support research and computational modeling of “later language acquisition”, with an emphasis for comparative assessment of oral and written language skills across early school grades.

Introduction
The use of naturalistic data to investigate child language features and development has a well- established tradition in L1 acquisition research. The most notable example is the CHILDES database (MacWhinney, 2000), which contains transcripts of spoken interactions involving children of different ages for over 25 languages, Italian included. Yet, CHILDES data refer especially to preschool children, with only a minor section dedicated to their older mates, thus making this resource less adequate for studying how language skills evolve during early schooling. The rapid and remarkable changes children’s language undergoes before age five justify the amount of research for the earliest stages of acquisition. However, over the last two decades also “later language acquisition” has gained increasing interest (Tolchinsky, 2004), prompted by the awareness that “becoming a native speaker is a rapid and highly efficient process, but becoming a proficient speaker takes a long time” (Berman, 2004). Indeed, under explicit teaching language keeps growing through school–age years in a way that affects all domains and modalities (Koutsoftas, 2013). Regarding the methodological approach to inspect children’s data, more attention has been recently paid to text analysis techniques drawn from computational linguistics and Natural Language Processing (NLP). The use of a statistical parser is reported e.g. by Sagae et al. (2005) and Lu (2009) to automate sophisticated measures of syntactic development, reaching performances comparable to those obtained by manual annotation. Computational methods are also employed in diagnostic settings, e.g. to identify markers of Autism Spectrum Disorders in children’s speech by integrating features from automatic morpho–syntactic and syntactic annotation (Prud’hommeaux and Roark, 2011), as well as metrics of semantic similarity (Rouhizadeh et al., 2015). Despite the focus of this paper is on the resource, we will also present preliminary analyses aiming at showing how a NLP perspective applied to a corpus like ISACCO can serve as the starting point to conduct computational explorations at multiple levels, which may become particularly useful in view of their applicability to large–scale corpora. It should be possible to test the effect of the diamesic variation on the linguistic complexity of children’s texts and to assess changes across schooling levels (cf. section 3.1). The same can be done with respect to the “content”, to evaluate whether these variables affect text comprehension and recall. To this aim, the output of an ontology learning system can provide a mean to compare the quantity of ’matched’ ideas between the child’s retelling and the content of the heard story (cf. section 3.2), so that to identify patterns of typical development to be used for comparison e.g. in clinical settings, with children showing atypical language development.

Conclusion
We presented ISACCO, a new resource for the Italian language containing oral and written retellings of children attending the primary school. We showed the potentiality of NLP-based analyses to inspect child language features, both with respect to linguistic and content structure, as well as in relation to diachronic and diamesic variations. Ongoing work is devoted to enlarge the corpus, also in a longitudinal perspective, to elaborate a qualitative analysis of linguistic errors by also looking comparatively at other learner corpora, and to evaluate the impact of child language features on standard linguistic annotation tools.