Chen Wang
ABSTRACT
Recently, learning to rank algorithms have become a popular and effective tool for ordering objects (e.g. terms) according to their degrees of importance. The contribution of this paper is that we propose a simple and fast learning to rank model RankBayes and embed it in the co-training framework. The detailed proof is given that Naïve Bayes algorithm can be used to implement a learning to rank model. To solve the problem of two-model inconsistency, an ingenious approach is put forward to rank all the phrases by making use of the labeled results of two RankBayes models. Experimental results show that the proposed approach is promising in solving ranking problems.
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Digital Library
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- Zhao, C., Yan, J. and Liu, N. 2008. Improve Web Search Ranking by Co-Ranking SVM. In proceedings of Fourth International Conference on Natural Computation. Google ScholarDigital Library
Index Terms
- CoRankBayes: bayesian learning to rank under the co-training framework and its application in keyphrase extraction
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