Name
Abstract | ||
|---|---|---|
This paper presents a new probabilistic approachto document retrieval based on the assumption thata Markov process can explain the process by whichhumans rank the relevance of documents to queries.The model ranks documents for retrieval based on theirprobability 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. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/ICDAR.2003.1227852 | International Conference on Document Analysis and Recognition |
Keywords | Field | DocType |
available databases,recall performance equivalent,new probabilistic approachto document,new algorithm,training method,Latent Semantic Analysis,assumption thata Markov process,Markov Model Document Retrieval | Markov process,Computer science,Artificial intelligence,Natural language processing,Probabilistic logic,Document retrieval,Markov algorithm,Pattern recognition,Information retrieval,Markov model,Markov chain,Latent semantic analysis,Hidden Markov model | Conference |
ISSN | ISBN | Citations |
1520-5363 | 0-7695-1960-1 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael P. Perrone | 1 | 76 | 16.50 |
| Alessandro Vinciarelli | 2 | 1682 | 104.77 |