Paper: Shallow Semantic Analysis of Interactive Learner Sentences

ACL ID W13-1702
Title Shallow Semantic Analysis of Interactive Learner Sentences
Venue Innovative Use of NLP for Building Educational Applications
Year 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 Motivation While there is much current work on analyzing learner language, it usually focuses on grammati- cal error detection and correction (e.g., Dale et al., 2...