A Simple Baseline for Discriminating Similar Languages

This paper describes an approach to discriminating similar languages using wordand characterbased features, submitted as the Queen Mary University of London entry to the Discriminating Similar Languages shared task. Our motivation was to investigate how well a simple, datadriven, linguistically naive method could perform, in order to provide a baseline by which more linguistically complex or knowledge-rich approaches can be judged. Using a standard supervised classifier with word and character n-grams as features, we achieved over 90% accuracy in the test; on fixing simple file handling and feature extraction bugs, this improved to over 95%, comparable to the best submitted systems. Similar accuracy is achieved using only word unigram features.