Current best-match ranking (BMR) systems perform well but cannot handle word mismatch between a query and a document. The best known alternative ranking method, hierarchical clustering-based ranking (HCR), seems to be more robust than BMR with respect to this problem, but it is hampered by theoretical and practical limitations. We present an approach to document ranking that explicitly addresse...

This paper describes the approaches and results of our system for the NTCIR-10 INTENT task. We present some methods for Subtopic Mining subtask and Document Ranking subtask. In the Subtopic Mining subtask, we employ a voting method to rank candidate subtopics and semantic resource HowNet was used to merge those candidate subtopics which may impact diversity. In the Document Ranking Subtask, we ...

This paper provides an overview of the NTCIR-10 INTENT-2 task (the second INTENT task), which comprises the Subtopic Mining and the Document Ranking subtasks. INTENT-2 attracted participating teams from China, France, Japan and South Korea – 12 teams for Subtopic Mining and 4 teams for Document Ranking (including an organisers’ team). The Subtopic Mining subtask received 34 English runs, 23 Chi...

The quality of the ranking function is an important factor that determines the quality of the Information Retrieval system. Each document is assigned a score by the ranking function; the score indicates the likelihood of relevance of the document given a query. In the vector space model, the ranking function is defined by a mathematic expression such as: ∑ ∈ = q t d q score ) , ( tf(t in d) * i...

To fight the problem of information overload in huge information sources like large document repositories, e. g. citeseer, or internet websites you need a selection criteria: some kind of ranking is required. Ranking methods like PageRank use the document reference network structure. However, these rankings do not distinguish different reference semantics. We enhance these rankings by incorpora...