The paper reports on the first steps in developing a time-stamped multimodal dataset of reading data by Bulgarian children. Data are being collected, structured and analysed by means of ReadLet, an innovative infrastructure for multimodal language data collection that uses a tablet as a reader’s front-end. The overall goal of the project is to quantitatively analyse the reading skills of a sample of early Bulgarian readers collected over a two-year period, and compare them with the reading data of early readers of Italian, collected using the same protocol. We illustrate design issues of the experimental protocol, as well as the data acquisition process and the post-processing phase of data annotation/augmentation. To evaluate the potential and usefulness of the Bulgarian dataset for reading research, we present some preliminary statistical analyses of our recently collected data. They show robust convergence trends between Bulgarian and Italian early reading development stages.

The paper presents the design and construction of a time-stamped multimodal dataset for reading research, including multiple time-aligned temporal signals elicited with four experimental trials of connected text reading by both child and adult readers. We present the experimental protocols, as well as the data acquisition process and the post-processing phase of data annotation/augmentation. To evaluate the potential and usefulness of a time-aligned multimodal dataset for reading research, we present a few statistical analyses showing the correlation and complementarity of multimodal time-series of reading data, as well as some results of modelling adults’ reading data by integrating different modalities. The total dataset size amounts to about 2.5 GByte in compressed format.

Assessing reading skills is a laborious and time-consuming task, which requires monitoring a variety of interlocked abilities, ranging from accurate word rendering, reading fluency and lexical access, to linguistic comprehension, and interpretation, management and inference of complex events in working memory. No existing software, to our knowledge, is able to cover and integrate reading performance monitoring, instant feedback, personalised potentiation and intelligent decision support to teachers and speech therapists, assessment of response to intervention. NLP and ICT technologies can make such an ambitious platform an achievable target.

The lexicon is the store of words in long-term memory. Any attempt at modelling lexical competence must take issues of string storage seriously. In the present contribution, we discuss a few desiderata that any biologically-inspired computational model of the mental lexicon has to meet, and detail a multi-task evaluation protocol for their assessment. The proposed protocol is applied to a novel computational architecture for lexical storage and acquisition, the """"Topological Temporal Hebbian SOMs"""" (T2HSOMs), which are grids of topologically organised memory nodes with dedicated sensitivity to time-bound sequences of letters. These maps can provide a rigorous and testable conceptual framework within which to provide a comprehensive, multi-task protocol for testing the performance of Hebbian self-organising memories, and a comprehensive picture of the complex dynamics between lexical processing and the acquisition of morphological structure.

In this paper, we reported experiments of unsupervised automatic acquisition of Italian and English verb subcategorization frames (SCFs) from general and domain corpora. The proposed technique operates on syntactically shallow-parsed corpora on the basis of a limited number of search heuristics not relying on any previous lexico-syntactic knowledge about SCFs. Although preliminary, reported results are in line with state-of-the-art lexical acquisition systems. The issue of whether verbs sharing similar SCFs distributions happen to share similar semantic properties as well was also explored by clustering verbs that share frames with the same distribution using the Minimum Description Length Principle (MDL). First experiments in this direction were carried out on Italian verbs with encouraging results.

The paper reports on a detailed quantitative analysis of distributional language data of both Italian and Czech, highlighting the relative contribution of a number of distributed grammatical factors to sentence-based identification of subjects and direct objects. The work is based on a Maximum Entropy model of stochastic resolution of grammatical conflicting constraints, and is demonstrably capable of putting explanatory theoretical accounts to the challenging test of an extensive, usage-based empirical verification.

In this paper we present an original approach to natural language query interpretation which has been implemented withinthe FuLL (Fuzzy Logic and Language) Italian project of BC S.r.l. In particular, we discuss here the creation of linguisticand ontological resources, together with the exploitation of existing ones, for natural language-driven database access andretrieval. Both the database and the queries we experiment with are Italian, but the methodology we broach naturally extends to other languages.