Reasoning about similarity queries in text retrieval tasks

ABSTRACT

In many text retrieval tasks, it is highly desirable to obtain a "similarity profile" of the document collection for a given query. We propose sampling-based techniques to address this need, using calibration techniques to improve the accuracy. Experimental results confirm the effectiveness of the proposed approaches.

REFERENCES

• 1: Andrei Broder , Marcus Fontura , Vanja Josifovski , Ravi Kumar , Rajeev Motwani , Shubha Nabar , Rina Panigrahy , Andrew Tomkins , Ying Xu, Estimating corpus size via queries, Proceedings of the 15th ACM international conference on Information and knowledge management, November 06-11, 2006, Arlington, Virginia, USA [doi>10.1145/1183614.1183699]
• 2: James P. Callan , Zhihong Lu , W. Bruce Croft, Searching distributed collections with inference networks, Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, p.21-28, July 09-13, 1995, Seattle, Washington, United States [doi>10.1145/215206.215328]
• 3: Luis Gravano , Hector Garcia-Molina, Generalizing GlOSS to Vector-Space Databases and Broker Hierarchies, Proceedings of the 21th International Conference on Very Large Data Bases, p.78-89, September 11-15, 1995

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Algorithms, Experimentation

Keywords:
calibration, query evaluation, sampling, text retrieval