Predicting Word Complexity Based on Corpus Frequency
We introduce a word frequency based classifier for the SemEval 2016 complex word identification task #11) Words with lower frequency are predicted as complex based on a threshold optimized for G score. We examine three different corpora for calculating frequencies and find English Wikipedia to perform best (ranked 13th on the SemEval task) followed by the Google Web Corpus and lastly Simple English Wikipedia. Bagging is also shown to slightly improve the performance of the classifier. Overall, we find word frequency to be a strong predictor of complexity. On the SemEval "test" set, a frequency classifier that uses the optimal frequency threshold performs on par with the best submitted system and a system trained using only 500 labeled examples split from the test set achieves results that are only slightly below the best submitted system.