  Computational research on error detection in second language speakers has
mainly addressed clear grammatical anomalies typical to learners at the
beginner-to-intermediate level. We focus instead on acquisition of subtle
semantic nuances of English indefinite pronouns by non-native speakers at
varying levels of proficiency. We first lay out theoretical, linguistically
motivated hypotheses, and supporting empirical evidence on the nature of the
challenges posed by indefinite pronouns to English learners. We then suggest
and evaluate an automatic approach for detection of atypical usage patterns,
demonstrating that deep learning architectures are promising for this task
involving nuanced semantic anomalies.
