Literature survey is a time-consuming process as researchers spend a lot of time in
searching the papers of interest. While search engines can be useful in finding papers
that contain a certain set of keywords, one still has to go through these papers in order
to decide whether they are of interest. On the other hand, one can quickly decide
which papers are of interest if each one of them is labelled with a category. The process
of labelling each paper with a category is termed paper categorization, an instance of
a more general problem called text classification. In this thesis, we presented a text
classifier called Iris that makes use of the popular Naive Bayes algorithm. With Iris,
we were able to (1) evaluate Naive Bayes using a number of popular datasets, (2)
propose a GUI for assisting users with document categorization and searching, and
(3) demonstrate how the GUI can be utilized for paper categorization and searching. / Graduate / 0984
| Identifer | oai:union.ndltd.org:uvic.ca/oai:dspace.library.uvic.ca:1828/4564 |
| Date | 29 April 2013 |
| Creators | Cui, Man |
| Contributors | Wadge, W. W. |
| Source Sets | University of Victoria |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
| Rights | Available to the World Wide Web |
Page generated in 0.0026 seconds