  Developments in the educational landscape have spurred greater interest in
the problem of automatically scoring short answer questions. A recent shared
task on this topic revealed a fundamental divide in the modeling approaches
that have been applied to this problem, with the best-performing systems split
between those that employ a knowledge engineering approach and those that
almost solely leverage lexical information (as opposed to higher-level
syntactic information) in assigning a score to a given response. This paper
aims to introduce the NLP community to the largest corpus currently available
for short-answer scoring, provide an overview of methods used in the shared
task using this data, and explore the extent to which more
syntactically-informed features can contribute to the short answer scoring task
in a way that avoids the question-specific manual effort of the knowledge
engineering approach.
