Paper: Feature Engineering in the NLI Shared Task 2013: Charles University Submission Report

ACL ID W13-1730
Title Feature Engineering in the NLI Shared Task 2013: Charles University Submission Report
Venue Innovative Use of NLP for Building Educational Applications
Session
Year 2013
Authors

Our goal is to predict the first language (L1) of English essays?s authors with the help of the TOEFL11 corpus where L1, prompts (top- ics) and proficiency levels are provided. Thus we approach this task as a classification task employing machine learning methods. Out of key concepts of machine learning, we fo- cus on feature engineering. We design fea- tures across all the L1 languages not making use of knowledge of prompt and proficiency level. During system development, we experi- mented with various techniques for feature fil- tering and combination optimized with respect to the notion of mutual information and infor- mation gain. We trained four different SVM models and combined them through majority voting achieving accuracy 72.5%.