The semi-supervised approach circumvents the need to include word n-gram features from any tweets, and builds upon the successful usage of word representations (Collobert et al., 2011) , and word clusters (Lin & Wu, 2009; Miller et al., 2004; Ratinov & Roth, 2009;<cite> Turian et al., 2010)</cite> for NER by utilizing large amounts of unlabelled data or models pre-trained on a large vocabulary.