An evaluation of phrasal and clustered representations on a text categorization task
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
A probabilistic model of information retrieval: development and comparative experiments
Information Processing and Management: an International Journal
A probabilistic model of information retrieval: development and comparative experiments Part 2
Information Processing and Management: an International Journal
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Feature Engineering for Text Classification
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Identifying comparative sentences in text documents
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Deploying Approaches for Pattern Refinement in Text Mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
A two-stage text mining model for information filtering
Proceedings of the 17th ACM conference on Information and knowledge management
Selected new training documents to update user profile
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
| Hi-index | 0.00 |
Over the years, people have often held the hypothesis that negative feedback should be very useful for largely improving the performance of information filtering systems; however, we have not obtained very effective models to support this hypothesis. This paper, proposes an effective model that use negative relevance feedback based on a pattern mining approach to improve extracted features. This study focuses on two main issues of using negative relevance feedback: the selection of constructive negative examples to reduce the space of negative examples; and the revision of existing features based on the selected negative examples. The former selects some offender documents, where offender documents are negative documents that are most likely to be classified in the positive group. The later groups the extracted features into three groups: the positive specific category, general category and negative specific category to easily update the weight. An iterative algorithm is also proposed to implement this approach on RCV1 data collections, and substantial experiments show that the proposed approach achieves encouraging performance.