Shallow Semantic Analysis of Interactive Learner Sentences
Levi King, Markus Dickinson · 2013
Focusing on applications for analyzing learner language which evaluate semantic appropri-ateness and accuracy, we collect data from a task which models some aspects of interac-tion, namely a picture description task (PDT). We parse responses to the PDT into depen-dency graphs with an an off-the-shelf parser, then use a decision tree to classify sentences into syntactic types and extract the logical sub-ject, verb, and object, finding 92 % accuracy in such extraction. The specific goal in this paper is to examine the challenges involved in ex-tracting these simple semantic representations from interactive learner sentences. 1