Detecting near-duplicates in large-scale short text databases

  • Authors:

  • Caichun Gong;Yulan Huang;Xueqi Cheng;Shuo Bai

  • Affiliations:

  • Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P.R.C. and Graduate School of Chinese Academy of Sciences, Beijing, P.R.C.;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P.R.C. and Graduate School of Chinese Academy of Sciences, Beijing, P.R.C.;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P.R.C.;Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P.R.C.

  • Venue:
  • PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
  • Year:
  • 2008

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Abstract

Near-duplicates are abundant in short text databases. Detecting and eliminating them is of great importance. SimFinder proposed in this paper is a fast algorithm to identify all near-duplicates in large-scale short text databases. An ad hoc term weighting scheme is employed to measure each term's discriminative ability. A certain number of terms with higher weights are seletect as features for each short text. SimFinder generates several fingerprints for each text, and only texts with at least one fingerprint in common are compared with each other. An optimization procedure is employed in SimFinder to make it more efficient. Experiments indicate that SimFinder is an effective solution for short text duplicate detection with almost linear time and storage complexity. Both precision and recall of SimFinder are promising.