2010 MiningPositiveandNegativePatter

• (Li et al., 2010) ⇒ Yuefeng Li, Abdulmohsen Algarni, and Ning Zhong. (2010). “Mining Positive and Negative Patterns for Relevance Feature Discovery.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835900

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%22Mining+positive+and+negative+patterns+for+relevance+feature+discovery%22+2010
• http://portal.acm.org/citation.cfm?id=1835900&preflayout=flat#citedby

Quotes

Abstract

It is a big challenge to guarantee the quality of discovered relevance features in text documents for describing user preferences because of the large number of terms, patterns, and noise. Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, people have often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences, but many experiments do not support this hypothesis. The innovative technique presented in paper makes a breakthrough for this difficulty. This technique discovers both positive and negative patterns in text documents as higher level features in order to accurately weight low-level features (terms) based on their specificity and their distributions in the higher level features. Substantial experiments using this technique on Reuters Corpus Volume 1 and TREC topics show that the proposed approach significantly outperforms both the state-of-the-art term-based methods underpinned by Okapi BM25, Rocchio or Support Vector Machine and pattern based methods on precision, recall and F measures.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 MiningPositiveandNegativePatter	Abdulmohsen Algarni Ning Zhong Yuefeng Li			Mining Positive and Negative Patterns for Relevance Feature Discovery		KDD-2010 Proceedings		10.1145/1835804.1835900		2010