This paper addresses the issue of automatically extracting keyphrases from document. Previously, this problem was formalized as classification and learning methods for classific...
This paper studies global ranking problem by learning to rank methods. Conventional learning to rank methods are usually designed for `local ranking', in the sense that the r...
Tao Qin, Tie-Yan Liu, Xu-Dong Zhang, De-Sheng Wang...
Probabilistic retrieval models usually rank documents based on a scalar quantity. However, such models lack any estimate for the uncertainty associated with a document’s rank. Fu...
Jianhan Zhu, Jun Wang, Michael J. Taylor, Ingemar ...
SimRank has been considered as one of the promising link-based ranking algorithms to evaluate similarities of web documents in many modern search engines. In this paper, we investi...
Abstract. While the Probability Ranking Principle for Information Retrieval provides the basis for formal models, it makes a very strong assumption regarding the dependence between...
Guido Zuccon, Leif Azzopardi, Keith van Rijsbergen