Garuda & Bhasha at semeval-2016 task 11: Complex Word Identification using aggregated learning models

Abstract

This paper describes aggregated learning models for Complex Word Identification (CWI) task in SemEval 2016. The work focused on selecting the features that determine complexity of words and used different combinations of support vector machine (SVM) and decision tree (DT) techniques for classification. These classifiers were pipelined with pre-processing and postprocessing blocks which helped improving accuracy of systems, though had little impact on recall. Four systems were evaluated on the test set; SVM and DT systems by team Bhasha achieved G score of 0.529 and 0.508 respectively and SVM&DT and SVMPP systems by team Garuda achieved G scores of 0.360 and 0.546 respectively.