A question answering system developed as a project in a natural language processing course

Abstract

This paper describes the Question Answering System constructed during a one semester graduate-level course on Natural Language Processing (NLP). We hypothesized that by using a combination of syntactic and semantic features and machine learning techniques, we could improve the accuracy of question answering on the test set of the Remedia corpus over the reported levels. The approach, although novel, was not entirely successful in the time frame of the course.