Using Density Estimation to Improve Text Categorization
- Using Density Estimation to Improve Text Categorization
- Sable, Carl
- Computer Science
- Persistent URL:
- Columbia University Computer Science Technical Reports
- Part Number:
- Department of Computer Science, Columbia University
- Publisher Location:
- New York
- This paper explores the use of a statistical technique known as density estimation to potentially improve the results of text categorization systems which label documents by computing similarities between documents and categories. In addition to potentially improving a system's overall accuracy, density estimation converts similarity scores to probabilities. These probabilities provide confidence measures for a system's predictions which are easily interpretable and could potentially help to combine results of various systems. We discuss the results of three complete experiments on three separate data sets applying density estimation to the results of a TF*IDF/Rocchio system, and we compare these results to those of many competing approaches.
- Computer science
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- Suggested Citation:
- Carl Sable, Kathleen McKeown, Vasileios Hatzivassiloglou, 2002, Using Density Estimation to Improve Text Categorization, Columbia University Academic Commons, https://doi.org/10.7916/D8ZW1Z30.