However, since new named entities arise regularly, it becomes increasingly difficult to maintain an up-to-date dictionary and/or adapt a named entity classifier to a new domain; for example, sequence labeling techniques that use feature templates (Finkel et al., 2005; Sarawagi and Cohen, 2004) are not robust for unknown named entities because their feature space is very sparse<cite> (Primadhanty et al., 2015)</cite> . Therefore, in this paper, we propose the use of matrix factorization for named entity classification to consider the relationships between sparse features.