Learning features through feedback for blog distillation

  • Authors:

  • Dehong Gao;Renxian Zhang;Wenjie Li;Yiu Keung Lau;Kam Fai Wong

  • Affiliations:

  • The Hong Kong Polytechnic University, Hong Kong, China;The Hong Kong Polytechnic University, Hong Kong, China;The Hong Kong Polytechnic University, Hong Kong, China;City University of Hong Kong, Hong Kong, China;The Chinese University of Hong Kong, Hong Kong, China

  • Venue:
  • Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
  • Year:
  • 2011

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Abstract

The paper is focused on blogosphere research based on the TREC blog distillation task, and aims to explore unbiased and significant features automatically and efficiently. Feedback from faceted feeds is introduced to harvest relevant features and information gain is used to select discriminative features. The evaluation result shows that the selected feedback features can greatly improve the performance and adapt well to the terabyte data.