Collaborative ranking and collaborative clustering
Heng Ji, Zheng Chen · 2013
Ranking and are two important problems in machine learning and have wide applications in Natural Language Processing (NLP). A problem is typically formulated as a collection of candidate objects with respect to a while a problem is formulated as organizing a set of instances into groups such that members in each group share some similarity while members across groups are dissimilar. In this thesis, we introduce schemes into and problems, and name them as collaborative ranking and collaborative clustering respectively. Contrast to the tradition non-collaborative schemes, leverages strengths from multiple query collaborators and ranker collaborators while leverages strengths from multiple instance collaborators and clusterer collaborators. We select several typical NLP problems as our case studies including entity linking, document and name entity clustering.