ABSTRACT
In this paper, we propose a document retrieval system with a light-weight feedback method for reconstructing a document vector space, which is developed on a Feature Extraction Model (FEM). FEM makes it possible to realize a light-weight creation of vector spaces by feature terms extracted from the pre-prepared documents and we can apply the feedback method dynamically to reconstruct the vector spaces based on intensions of users. Retrieval results can be improved through the proposed feedback process because the distributions of documents on the reconstructed vector space are arranged properly according to purposes and interests of users.
- Baeza-Yates R., Ribeiro-Neto, B. Modern Information Retrieval, Addison Wesley, 1999. Google ScholarDigital Library
- Chen, X. and Kiyoki, Y. A. Dynamic Retrieval Space Creation Method for Semantic Information Retrieval, Information Modelling and Knowledge Bases, Vol. XVI, IOS Press, 46--63, 2005.Google Scholar
- Deerwester, S., Dumais, S., Furnas, G. W. Landauer, T. K. and Harshman, R., Indexing by latent semantic analysis, Journal of the American Society for Information Science, Vol. 41, No. 6, 391--407, 1990.Google ScholarCross Ref
- NTCIR: http://research.nii.ac.jp/ntcir/Google Scholar
- Yu, L. and Liu, H. Efficient Feature Selection via Analysis of Relevance and Redundancy, Journal of Machine Learning Research, Vol.5, 1205--1224, MIT Press, 2004. Google ScholarDigital Library
Index Terms
- A light-weight feedback method for reconstructing a document vector space on a feature extraction model
Recommendations
A framework for a feedback process to analyze and personalize a document vector space in a feature extraction model
In this paper, we present a framework for a feedback process to implement a highly accurate document retrieval system. In the system, a document vector space is created dynamically to implement retrieval processing. The retrieval accuracy of the system ...
Comparison of Performance for SVM Based Relevance Feedback Document Retrieval in Several Vector Space Models
WI-IAT '08: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03We investigate the following data mining problems from the document retrieval: From a large data set of documents, we need to find documents that relate to human interest as few iterations of human testing or checking as possible. Ineach iteration a ...
Non-relevance Feedback for Document Retrieval
KAM '09: Proceedings of the 2009 Second International Symposium on Knowledge Acquisition and Modeling - Volume 02We need to find documents that relate to human interesting from a large data set of documents. The relevance feedback method needs a set of relevant and non-relevant documents to work usefully. However, the initial retrieved documents, which are ...
Comments