PAISÀ is a Creative Commons licensed, large web corpus of contemporary Italian. We describe the design, harvesting, and processing steps involved in its creation.

No abstract

We offer a computational model of gaze planning during reading that consists of two main components: a lexical representation network, acquiring lexical representations from input texts (a subset of the Italian CHILDES database), and a gaze planner, designed to recognize written words by mapping strings of characters onto lexical representations. The model implements an active sensing strategy that selects which characters of the input string are to be fixated, depending on the predictions dynamically made by the lexical representation network. We analyze the developmental trajectory of the system in performing the word recognition task as a function of both increasing lexical competence, and correspondingly increasing lexical prediction ability. We conclude by discussing how our approach can be scaled up in the context of an active sensing strategy applied to a robotic setting.

Many tasks in language analysis are described as the maximally economic mapping of one level of linguistic representation onto another such level. Over the past decade, many diOE erent machine-learning strategies have been developed to automatically induce such mappings directly from data. In this paper, we contend that the way most learning algorithms have been applied to problems of language analysis re¯ects a strong bias towards a compositional (or biunique) model of interlevel mapping. Although this is justi® ed in some cases, we contend that biunique inter-level mapping is not a jack of all trades. A model of analogical learning, based on a paradigmatic reanalysis of memorized data, is presented here. The methodological pros and cons of this approach are discussed in relation to a number of germane linguistic issues and illustrated in the context of three case studies: word pronunciation, word analysis, and word sense disambiguation. The evidence produced here seems to suggest that the brain is not designed to carry out the logically simplest and maximally economic way of relating form and function in language. Rather we propose a radical shift of emphasis in language learning from syntagmatic inter-level mapping to paradigmatically-constrained intra-level mapping.

A growing body of evidence in cognitive psychology and neuroscience suggests a deep interconnection between sensory-motor and language systems in the brain. Based on recent neurophysiological findings on the anatomo-functional organization of the fronto-parietal network, we present a computational model showing that language processing may have reused or co-developed organizing principles, functionality, and learning mechanisms typical of premotor circuit. The proposed model combines principles of Hebbian topological self-organization and prediction learning. Trained on sequences of either motor or linguistic units, the network develops independent neuronal chains, formed by dedicated nodes encoding only context-specific stimuli. Moreover, neurons responding to the same stimulus or class of stimuli tend to cluster together to form topologically connected areas similar to those observed in the brain cortex. Simulations support a unitary explanatory framework reconciling neurophysiological motor data with established behavioral evidence on lexical acquisition, access, and recall.