DocumentCode
2119739
Title
Notice of Retraction
Chinese Information Retrieval Using Clustering Expansion
Author
Guangqi Li ; Jinzhuo He
Author_Institution
Int. Sch. of Software, Wuhan Univ., Wuhan, China
fYear
2009
fDate
20-22 Sept. 2009
Firstpage
1
Lastpage
3
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
This paper describes a improved document expansion method in information retrieval. Using clustering expansion, we found amazing feedback based on the ordinary query expansion. The traditional information retrieval model has restrict on search related documents due to it only checks the existence of query terms in documents can not considering the context of documents. Now we retrieve documents by VSM and cluster the top-x documents to re-ranking the result set from the formore. Experiments show that the new method has realized a good advancement comparing with the traditional method.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
This paper describes a improved document expansion method in information retrieval. Using clustering expansion, we found amazing feedback based on the ordinary query expansion. The traditional information retrieval model has restrict on search related documents due to it only checks the existence of query terms in documents can not considering the context of documents. Now we retrieve documents by VSM and cluster the top-x documents to re-ranking the result set from the formore. Experiments show that the new method has realized a good advancement comparing with the traditional method.
Keywords
natural languages; query processing; Chinese information retrieval; VSM; clustering expansion method; document expansion method; query expansion; Clustering algorithms; Clustering methods; Computer science; Context modeling; Feedback; Frequency; Helium; Information retrieval; Partitioning algorithms; Performance analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Management and Service Science, 2009. MASS '09. International Conference on
Conference_Location
Wuhan
Print_ISBN
978-1-4244-4638-4
Type
conf
DOI
10.1109/ICMSS.2009.5302785
Filename
5302785
Link To Document