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Learning to pronounce a written word implies assigning a stress pattern to that word. This task can present a challenge for speakers of languages like Italian, in which stress information must often be computed from distributional properties of the language, especially for individuals learning Italian as a second language (L2). Here, we aimed to characterize the processes underlying the development of stress assignment in native English and native Chinese speakers learning L2 Italian. Both types of bilinguals produced evidence supporting a role of vocabulary size in modulating the type of distributional information used in stress assignment, with an early bias for Italian's dominant stress pattern being gradually replaced by use of associations between orthographic sequences and stress patterns in more advanced bilinguals. We also obtained some evidence for a transfer of stress assignment habits from the bilinguals’ native language to Italian, although only in English native speakers.

This paper summarises the contributions to EuroCALL’s CorpusCALL SIG Symposium for the year 2020. In line with this year’s EuroCALL conference theme, ‘CALL for widening participation’, the Symposium centred around the theme of Data-driven learning for languages other than English. This paper gives a brief overview of developments and challenges when using Data-Driven Learning (DDL) to teach French, German, Italian, and Spanish. As research suggests, a DDL approach has been effectively utilised to teach these languages. However, there are differences in available DDL resources and corpora for the respective languages that are appropriate for language teaching. The main challenges for future developments are also discussed.

This work introduces an automatic classification system for measuring the complexity level of a given Italian text under a linguistic point-of-view. The task of measuring the complexity of a text is cast to a supervised classification problem by exploiting a dataset of texts purposely produced by linguistic experts for second language teaching and assessment purposes. The commonly adopted Common European Framework of Reference for Languages (CEFR) levels were used as target classification classes, texts were elaborated by considering a large set of numeric linguistic features, and an experimental comparison among ten widely used machine learning models was conducted. The results show that the proposed approach is able to obtain a good prediction accuracy, while a further analysis was conducted in order to identify the categories of features that influenced the predictions.

This paper aims to shed light on how research findings stemming from Learner Corpus Research (LCR) can inform the development of Data-driven learning (DDL) pedagogical activities. By doing this, it seeks to show how the gap between corpora built to be used by linguists and those tailored for learners can be filled. It starts by defining what a corpus is and how second language learning studies can benefit from the research findings based on corpora, but also from the direct use of corpora in the classroom. Then, it provides an overview of the available native and learner corpora of Italian, and how corpora in general can be adapted for DDL purposes. Finally, it describes an example of how an LCR finding can be used to develop DDL activities. It concludes with some desiderata for the future.

In this article we outline the results of an evaluation of Data-driven learning (DDL) effects in relation to the development of Italian L2 phraseological competence. We do this on the basis on empirical data, by combining an external and objective perspective based on data elicited through a competence test, and an internal and subjective perspective based on data elicited through a student questionnaire. In the first case we refer to etic data, while in the second case we refer to emic data. Overall, the results indicate mild positive effects in terms of etic data, but stronger positive effects in terms of emic data. The article concludes by stressing the importance of combining two perspectives such as the ones adopted in this study, in order to be able to observe some of the many different aspects of educational effectiveness within a single, integrated framework.

Concordance-based Data-Driven Learning (DDL) aims to help second language learners infer language usage rules from language usage regularities. A number of DDL pedagogical treatments have focussed on phraseological units such as collocations, widely recognised as a central component of second language learning. This study evaluates DDL effects from an emic perspective, reflecting the learners’ perceived usefulness of the approach, as opposed to etic perspectives, representing changes in language competence as a result of the approach. It compares a group of Chinese learners and a group of Belgian learners of Italian as a Second Language/Foreign Language (SL/FL). The findings indicate that the Belgian students seem to have gained familiarity with the approach faster than the Chinese, though the latter seems to perceive greater long-term benefits of the approach, and are more favourable to future mobile phone applications. The study aims to shed light on possible learner-related differences in DDL treatments and on the insightfulness of emic data in assessing DDL effects.

The selection of texts for second language learning purposes typically relies on teachers’ and test developers’ individual judgment of the observable qualitative properties of a text. Little or no consideration is generally given to the quantitative dimension within an evidence-based framework of reproducibility. This study aims to fill the gap by evaluating the effectiveness of an automatic tool trained to assess text complexity in the context of Italian as a second language learning. A dataset of texts labeled by expert test developers was used to evaluate the performance of three classifier models (decision tree, random forest, and support vector machine), which were trained using linguistic features measured quantitatively and extracted from the texts. The experimental analysis provided satisfactory results, also in relation to which kind of linguistic trait contributed the most to the final outcome.

The sydnones 1a–c react with the 4,5-dihydro-v-triazoles 2a–c by long refluxing at 110– 140°C affording via non-isolable adducts with elimination of CO2 and N2 and rearrangement a mixture of 4,5-dihydropyrazoles 3a– c, 3-pyrazolecarbaldehyde anils 4a–d, and 3-pyrazolecarbaldehydes 5a–c. Compounds 3 can be deaminated to give 4 which can be hydrolyzed to yield the 3-pyrazolecarbaldehydes 5.– The sydnones 6a, b react similarly with 2a to give a mixture of 4-pyrazolecarbaldehyde anils 7a, b and 4-pyrazolecarbaldehydes 8a, b. The structural assignments are based on spectroscopic and X-ray diffraction data. v-Triazoline, XXIV.– Pyrazolcarbaldehyde aus 5-Amino-4,5-dihydro-4-methylen-v-triazolen und Sydnonen Die Sydnone 1a– c ergeben mit den 4,5-Dihydro-v-triazolen 2a– c nach langerem Ruckfluserhitzen auf 110– 140°C uber nicht isolierbare Primaraddukte nach Eliminierung von CO2 und N2 sowie nach Umlagerung ein Gemisch der 4,5-Dihydropyrazole 3a– c, 3-Pyrazolcarbaldehyd-anile 4a–d und 3-Pyrazolcarbaldehyde 5a–c. Die Verbindungen 3 konnen zu 4 desaminiert werden, und letztere lassen sich zu 3-Pyrazolcarbaldehyden 5 hydrolysieren.– Die Sydnone 6a, b reagieren ahnlich mit 2a unter Bildung eines Gemisches von 4-Pyrazolcarbaldehyd-anilen 7a, b und 4-Pyrazolcarbaldehyden 8a, b. Die Strukturzuweisungen stutzen sich auf spektroskopische Daten und Rontgenstrukturanalysen.

5-Amino-2,3,6,7-tetrahydrothiocines were reacted with arylazides yielding 1-aryl-9a-amino-1,3a,4,5,7,8,9,9a-octahydrothiocino[4,5-d]-1,2,3-triazoles which could be deaminated to 1-aryl-1,4,5,7,8,9-hexahydrothiocino-[4,5-d]-1,2,3-triazoles. Similarly, the above enamines yielded, with nitrile oxides, 3-aryl-9a-amino-3a,4,5,8,9,9a-hexahydrothiocino[4,5-d]isoxazoles which were transformed with acids into 3-aril-4,5,8,9-tetrahydrothiocino-[4,5-d]isoxazoles.

2-Aminobenzenethiol was condensed with alkyl α-chlorophenylhydrazonoglyoxylates yielding alkyl 2-(2-aminophenylthio)-2-arylhydrazonoglyoxylates which were cyclized to the corresponding 2-arylhydrazono-2,3-dihydro-4H-1,4-benzothiazin-3-ones. Starting from α-chloro-α-arylhydrazonoacetones the corresponding 2-arylhydrazono-3-methylbenzo-1,4-benzothiazines were formed.