Abstract:
In this paper a supervised topic model is proposed for rank learning. The original supervised topic model can only learn from positive samples. For rank learning problem,...View moreMetadata
Abstract:
In this paper a supervised topic model is proposed for rank learning. The original supervised topic model can only learn from positive samples. For rank learning problem, training data have different ranking labels. To solve this issue, we extend the supervised topic model and make it learn from training data with different ranking labels. The experiments show that the proposed topic models can find the hidden relationships among words, and have higher ranking accuracy than word based models. In addition, the supervised topic models have higher ranking accuracy than the unsupervised topic models.
Date of Conference: 12-17 July 2015
Date Added to IEEE Xplore: 01 October 2015
ISBN Information: