The problem of re-ranking initial retrieval results exploring the intrinsic structure of documents is widely researched in information retrieval (IR) and has attracted a considerable amount of time and study. However, one of the drawbacks is that those algorithms treat queries and documents separately. Furthermore, most of the approaches are predominantly built upon graph-based methods, which m...

In this paper, we address the problem of ranking clinical documents using centrality based approach. We model the documents to be ranked as nodes in a graph and place edges between documents based on their similarity. Given a query, we compute similarity of the query with respect to every document in the graph. Based on these similarity values, documents are ranked for a given query. Initially,...

The FRDC team participated in the IMine task of the NTCIR11, including subtopic mining and document ranking subtasks for Chinese language. In the subtopic mining subtask, we propose two methods to build the two-level hierarchy subtopics. Our methods gain high F-score and H-score respectively. In the document ranking subtask, we adopt various features for relevant webpage retrieval and document ...

In NTCIR-9, we participate in the Intent task, including both the Subtopic Mining subtask and the Document Ranking subtask. In the Subtopic Mining subtask, we mine subtopics from query logs and top results of the queries, and rank them based on their relevance to the query and the similarity between them. In the Document ranking Subtask, we diversify top search results using the mined subtopics...