Evaluating stages of development in second language French: A machine-learning approach

In English This paper describes a system to define and
evaluate development stages in second language
French. The identification of such
stages can be formulated as determining the
frequency of some lexical and grammatical
features in the learners’ production and how
they vary over time. The problems in this
procedure are threefold: identify the relevant
features, decide on cutoff points for the
stages, and evaluate the degree of success of
the model.
The system addresses these three problems.
It consists of a morphosyntactic analyzer
called Direkt Profil and a machine-learning
module connected to it. We first describe the
usefulness and rationale behind its development.
We then present the corpus we used
to develop the analyzer. Finally, we present
new and substantially improved results on
training machine-learning classifiers compared
to previous experiments (Granfeldt et
al., 2006). We also introduce a method to
select attributes in order to identify the most
relevant grammatical features.