Detecting Context Dependence in Exercise Item Candidates Selected from Corpora
This work addresses the need for more efficient and authentic sentence selection in language learning tools, though it is incremental as it builds on existing corpus-based methods.
The paper tackled the problem of automatically identifying sentences from corpora that can be used in isolation for language learning exercises by detecting context dependence, achieving an average precision of 0.76 and 80% agreement with human raters for context-independent sentences.
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.