One simple but powerful approach for further enhancing the performance is to utilize a large amount of unsupervised data to supplement supervised data.
an approach that involves incorporating 'clusteringbased word representations (CWR)' induced from unsupervised data as additional features of supervised learning has demonstrated substantial performance gains over state-of-the-art supervised learning systems in typical NLP tasks, such as named entity recognition (Lin and Wu, 2009;Turian et al., 2010) and dependency parsing (Koo et al., 2008).