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SemEval 2016 task 11 involved determining whether words in a sentence were complex orsimple for a cohort of people with English as a second language. Training data consistedof 200 annotated sentences, representing the combined judgements of 20 human annota-tors, such that if any annotator of the group labelled a word as complex, then it was con-sidered to be complex. Testing was based on single annotator judgements. Our system useda random forest classifier with a variety of features, the most important of which were termfrequency statistics garnered from four large corpora, and style lexicons built on two largecorpora. Minor features in the final system include the presence or absence of words invarious readability word lists; many other features we tried were not successful. Our rank-ing amongst submitted systems did not reflect the strength of our system, due to submitting afar from optimal weighting between complex and simple, but we show that when a more ap-propriate weighting is used, our system ranks amongst the best submitted systems.

Melbourne at SemEval 2016 Task 11: Classifying Type-level Word Complexity using Random Forests with Corpus and Word List Features