PRIS at 2009 Relevance Feedback track: Experiments in Language Model for Relevance Feedback

Abstract

This paper describes BUPT (pris) participation in Relevance Feedback Track 2009. The track has two phrases. In the first phrase, 5 documents are submitted based on the results of the k-means. In the second phrase, language model is used to relevance feedback for query expansion.

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 2009
Accession Number
ADA517722

Entities

People

  • Guang Chen
  • Hao Zhang
  • Jun Guo
  • Sanyuan Gao
  • Si Li
  • Xinsheng Li

Organizations

  • Peking University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Closed Loop Systems
  • Clustering
  • Engineering
  • Estimators
  • Feedback
  • Frequency
  • Information Operations
  • Intelligent Systems
  • Language
  • Machine Learning
  • Models
  • Pattern Recognition
  • Probability
  • Probability Distributions
  • Supervised Machine Learning

Readers

  • Computational Linguistics
  • Information Retrieval