Parsing di corpora di apprendenti di italiano:un primo studio su VALICO
Authors: Elisa Corino, Claudio Russo (Università di Torino, 2016)
Topic: Learner Corpora and Language Acquisition

Abstract
The modern body of learners are now usually noted for PoS. Less frequent is the syntactic parsing of learning varieties. This contribution proposes a first attempt at parsing applied to a subcorpus of VALICO, in an attempt to identify key elements that could serve to chart a route for syntactic labelling of Italian corpora as a foreign/second language (LS).

Introduction
It is now customary to read and write down the body of learners for Parts of Speech (PoS) and there are many examples of labeling errors; instead, the experiences of parsing and syntactic annotations of corporate learner are not as widespread. The reasons are many, but certainly due to the extreme unpredictability and markedness that characterizes the interlanguage of the learners. Despite the complexity of the operation, which necessarily requires a good dose of manual intervention, the parsing of a learner corpus can have positive repercussions in more than one area: in addition to the contribution for contexts that deal with computational linguistics in a precipio way, it becomes extremely useful for research on acquisition and learning, because it allows to more easily detect errors, deviations from the norm and distribution of syntactic categories and structures otherwise difficult to emerge by questioning a corpus labeled only for parts of the speech. Applying parsing processes to learning varieties is certainly not a novelty: the syntactic treatment of learner interlinguals has long been the focus of Computer Assisted Language Learning (CALL) and Intelligent Language Tutoring Systems (ILTS) systems (Vandeventer Faltin, 2003; Heift & Nicholson, 2001; Amaral & Meurers, 2011). In these cases the violation of the syntactic rules, such as the non-agreement between the subject and the verb, becomes a spy of the presence of an error to be reported to those who produce the text. Menzel & Schröder (1999) uses parameters that describe dependencies within the languages of learners, to which weighed constraints and robust parsing procedures are applied to define the deviation from the norm. The distance from canonical and unmarked structures must, however, be defined from a standard to which the learning varieties are traced. This is why the parser of the learner corpus must be applied in advance to a corpus of natives, possibly comparable to that of the learners. A common practice is then to bring wrong occurrences back to a target form, a manual procedure usually used by those corporate learners noted also for errors. Some attempts have been made so far mainly for German (Nivre et al., 2007, Lüdeling 2008, Ott & Zai 2010), but the terrain still seems unexplored with regard to the Italian landscape. This contribution is proposed as a first attempt to apply parsing procedures to a corpus of learners of Italian as a foreign language, to define its criticalities and benefits both from the computational point of view and from the study of learning varieties. In particular, some data extracted from the corpus VALICO1 will be taken into account, selected from the subbody defined according to the L1 of the learners. For this pilot study it was chosen to compare the results of the treatment of texts of Hispanophone and Germanophone learners, with the aim of identifying syntactic peculiarities of the interlingues of learners coming from typologically different languages: typically romance and therefore close to the Italian Lupin, representative of the Germanic area the other. 12 texts (four for each of the first three years of Italian study) were randomly extracted from the Germanophone subcorpus and 12 texts derived from the Hispanophone subcorpus, according to the same selection criteria. A total of 126 sentences were processed for the German group, for the Spanish group 78. The ultimate aim is to determine which deviations from the standard of either group are not correctly labelled, so as to trace a path that can lead to the definition of labelling rules to train the parser and improve its performance on this language variety.
VINCA, the corpus of natives paired with VALICO, will serve as a body of reference for the dependent parser developed among the tools of Turin University Linguistic Environment (TULE).

Conclusion
The experience gained in this first phase of the research and implementation of parsing on the corpus of learners VALICO has allowed to highlight the advantages and criticalities of the tools used. From the computational point of view, it has emerged that it is necessary to integrate resources and define the training protocols of the parser oriented to the treatment of that particular variety of language which is the interlingua of learners. A qualitative evaluation showed that errors caused by the omission of key syntactical elements for the definition of dependencies lead to errors in the same labeling, as happens on the occasion of the overexpansion of some forms to functions that they do not have, or still of the negative synergy between morphologically complex forms and spelling errors; other errors instead are treated in a problematic way. 
The difficulties in managing prepositional syntagms and widespread linguistic transfer phenomena, especially at the level of the verbal system, reveal how the identification of trends and the definition of some indications on the treatment of such constructions could greatly increase the accuracy of automatic labelling. Compared to the definition of an architecture that integrates syntactical parsing at PoS tagging, it is necessary to draw up a clear proposal for the definition of labelling rules; moreover, it remains to be determined whether it is really necessary to align with the choices made for other corporate learners in which a target hypothesis is defined that marks the objective structure of the production of the apprentice and highlights its distance from the version actually reported in the corpus.