Ranking Tweets by Labeled and Collaboratively Selected Pairs with Transitive Closure


  • Shenghua Liu Chinese Academy of Sciences
  • Xueqi Cheng Chinese Academy of Sciences
  • Fangtao Li Google Inc.




Microblog search, ranking tweets, co-training, semi-supervised learning, transitive closure


Tweets ranking is important for information acquisition in Microblog. Due to the content sparsity and lackof labeled data, it is better to employ semi-supervisedlearning methods to utilize the unlabeled data. However,most of previous semi-supervised learning methods donot consider the pair conflict problem, which means thatthe new selected unlabeled data may conflict with the labeled and previously selected data. It will hurt the learning performance a lot, if the training data contains manyconflict pairs. In this paper, we propose a new collaborative semi-supervised SVM ranking model (CSR-TC)with consideration of the order conflict. The unlabeleddata is selected based on a dynamically maintained transitive closure graph to avoid pair conflict. We also investigate the two views of features, intrinsic and contentrelevant features, for the proposed model. Extensive experiments are conducted on TREC Microblogging corpus. The results demonstrate that our proposed methodachieves significant improvement, compared to severalstate-of-the-art models.




How to Cite

Liu, S., Cheng, X., & Li, F. (2014). Ranking Tweets by Labeled and Collaboratively Selected Pairs with Transitive Closure. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1). https://doi.org/10.1609/aaai.v28i1.8896



Main Track: Machine Learning Applications