Markov Model Document Retrieval

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Seventh International Conference on Document Analysis and Recognition (ICDAR'03) - Volume 2

Edinburgh, Scotland

August 03-August 06

ISBN: 0-7695-1960-1

	ASCII Text	x
	Michael Perrone, Alessandro Vinciarelli, "Markov Model Document Retrieval," Document Analysis and Recognition, International Conference on, vol. 2, pp. 1223, Seventh International Conference on Document Analysis and Recognition (ICDAR'03) - Volume 2, 2003.

	BibTex	x
	@article{ 10.1109/ICDAR.2003.1227852, author = {Michael Perrone and Alessandro Vinciarelli}, title = {Markov Model Document Retrieval}, journal ={Document Analysis and Recognition, International Conference on}, volume = {2}, year = {2003}, isbn = {0-7695-1960-1}, pages = {1223}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDAR.2003.1227852}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Document Analysis and Recognition, International Conference on TI - Markov Model Document Retrieval SN - 0-7695-1960-1 SP EP A1 - Michael Perrone, A1 - Alessandro Vinciarelli, PY - 2003 KW - null VL - 2 JA - Document Analysis and Recognition, International Conference on ER -

Michael Perrone, IBM T.J. Watson Research Center

Alessandro Vinciarelli, Institut Dalle Molle d'Intelligence Artificielle Perceptive

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDAR.2003.1227852

This paper presents a new probabilistic approach to document retrieval based on the assumption that a Markov process can explain the process by which humans rank the relevance of documents to queries. The model ranks documents for retrieval based on their probability of relevance. Two training methods are presented. The model is compared with Latent Semantic Analysis (LSA) on two publicly available databases. The results show that the new algorithm achieves Precision/Recall performance equivalent to or better than LSA.

Citation:

Michael Perrone, Alessandro Vinciarelli, "Markov Model Document Retrieval," icdar, vol. 2, pp.1223, Seventh International Conference on Document Analysis and Recognition (ICDAR'03) - Volume 2, 2003

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