Name
Abstract | ||
|---|---|---|
This paper presents work that evaluates background knowl- edge for use in improving accuracy for text classification using Latent Semantic Indexing (LSI). LSI's singular value decomposition process can be performed on a combination of training data and background knowledge. Intuitively, the closer the background knowledge is to the classification task, the more helpful it will be in terms of creating a reduced space that will be effective in performing classification. Using a va- riety of data sets, we evaluate sets of background knowledge in terms of how close they are to training data, and in terms of how much they improve classification. |
Year | Venue | Keywords |
|---|---|---|
2005 | FLAIRS Conference | latent semantic indexing,singular value decomposition |
Field | DocType | Citations |
Training set,Singular value decomposition,Data set,Latent semantic indexing,Information retrieval,Computer science,Probabilistic latent semantic analysis,Natural language processing,Artificial intelligence | Conference | 0 |
PageRank | References | Authors |
0.34 | 4 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sarah Zelikovitz | 1 | 181 | 16.42 |
| Finella Marquez | 2 | 18 | 1.66 |