  The current mode of biomedical literature search is severely limited in
effectively finding information relevant to specialists. A potential approach
to solving this problem is exploratory search, which allows users to
interactively navigate through a vast document collection. As the first step
toward exploratory search for specialists in biomedicine, this paper develops a
methodology to evaluate quality of document clusters. For this purpose, we
incorporate human expertise into data set creation and evaluation framework by
leveraging MeSH terms as semantic anchors. In addition, we investigate the
benefit of full-text data for improving cluster quality.
