Prior work has shown that semi-supervised algorithms can perform decently for NER tasks with sparse labelled data (Blum, 1998; Carlson et al., 2010; Chapelle et al., 2009; Liang, 2005; <cite>Turian et al., 2010</cite>; Zhu & Goldberg, 2009 ). We leverage this fact in our SI model via the use of unsupervised word clusters, word representations, and refined gazetteers; all of which contributed to a cumulative increase in accuracy over our initial submission [ST] by ~11% when using the test data for evaluation.