Shallow Semantic Analysis of Interactive Learner Sentences

Focusing on applications for analyzing learner language which evaluate semantic appropriateness and accuracy, we collect data from a task which models some aspects of interaction, namely a picture description task (PDT). We parse responses to the PDT into dependency graphs with an an off-the-shelf parser, then use a decision tree to classify sentences into syntactic types and extract the logical subject, verb, and object, finding 92% accuracy in such extraction. The specific goal in this paper is to examine the challenges involved in extracting these simple semantic representations from interactive learner sentences.