Automatic text categorization applied to E-mail
Approved for public release, distribution is unlimited === d investigated its application upon categorizing emails. The categorization approach is derived from an instanced-based learning method that explores conditional probabilities of particular words. The effectiveness of the author's categ...
| Main Author: | |
|---|---|
| Other Authors: | |
| Published: |
Monterey, California. Naval Postgraduate School
2012
|
| Online Access: | http://hdl.handle.net/10945/5142 |
| Summary: | Approved for public release, distribution is unlimited === d investigated its application upon categorizing emails. The categorization approach is derived from an instanced-based learning method that explores conditional probabilities of particular words. The effectiveness of the author's categorization approach using collections from a set of emails is then evaluated and assigned a numerical score based upon precision and recall. Precision was 65% while recall was 17%. The author's experiments indicated automatic categorization of incoming emails at the client level can categorize email, but is difficult when not using a standardized corpus. Word frequency is valuable, but should be used in combination with other methods such as phrase extraction for a higher level of performance. |
|---|