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Improving Text Classification with LSI Using Background Knowledge (2001)
| Venue: | IJCAI01 Workshop Notes on Text Learning: Beyond Supervision |
| Citations: | 8 - 0 self |
BibTeX
@INPROCEEDINGS{Zelikovitz01improvingtext,
author = {Sarah Zelikovitz and Haym Hirsh},
title = {Improving Text Classification with LSI Using Background Knowledge},
booktitle = {IJCAI01 Workshop Notes on Text Learning: Beyond Supervision},
year = {2001},
pages = {113--118}
}
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Abstract
We present work in progress that uses Latent Semantic Indexing (LSI) in conjunction with background knowledge and unlabeled examples to improve text classification accuracy. The singular value decomposition (SVD) that is performed by LSI is done on an expanded term by document matrix that includes the labeled training examples as well as the unlabeled examples. We report classification accuracy on different data sets both with and withoutthe inclusion of background knowledge and compare it to other known work.
Keyphrases
text classification lsi using background knowledge unlabeled example background knowledge singular value decomposition classification accuracy text classification accuracy labeled training example document matrix present work latent semantic indexing withoutthe inclusion known work different data set
