  We explore the factors influencing the dependence of single sentences on
their larger textual context in order to automatically identify candidate
sentences for language learning exercises from corpora which are presentable in
isolation. An in-depth investigation of this question has not been previously
carried out. Understanding this aspect can contribute to a more efficient
selection of candidate sentences which, besides reducing the time required for
item writing, can also ensure a higher degree of variability and authenticity.
We present a set of relevant aspects collected based on the qualitative
analysis of a smaller set of context-dependent corpus example sentences.
Furthermore, we implemented a rule-based algorithm using these criteria which
achieved an average precision of 0.76 for the identification of different
issues related to context dependence. The method has also been evaluated
empirically where 80% of the sentences in which our system did not detect
context-dependent elements were also considered context-independent by human
raters.
