TUTA1 at the NTCIR-11 IMine Task

In this paper, we detail our participation in two subtasks: subtopic mining and document ranking of the NTCIR-11 IMine task. In the subtopic mining subtask, to discover the latent hierarchy among query-like strings, our key idea is to structurally parse query-like strings by characterizing pairwise dependency in the bag-of-units perspective. Then the clustering algorithm (i.e., affinity propagation) and the Sainte-Laguë algorithm are used to obtain the target list that represents a two-level hierarchy of subtopics. In the document ranking subtask, we deploy the newly proposed 0-1 MSKP model for diversified document ranking against unclear topics. A subset of documents are optimally chosen like filling up multiple subtopic knapsacks.