Classifier Stacking and Voting for Text Filtering

Description:

This article discusses classifier stacking and voting for text filtering.

Creator(s): Mihalcea, Rada, 1974-
Creation Date: November 2002
Partner(s):
UNT College of Engineering
Collection(s):
UNT Scholarly Works
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Creator (Author):
Mihalcea, Rada, 1974-

University of North Texas

Publisher Info:
Place of Publication: [Gaithersburg, Maryland]
Date(s):
  • Creation: November 2002
Description:

This article discusses classifier stacking and voting for text filtering.

Degree:
Note:

Abstract: This paper summarizes the approach and the results of the TextCat system participating in the Filtering track in the Text Retrieval Conference 2002. The system relies primarily on statistical methods, and was designed with the main purpose of having a backbone system in which we can further integrate semantic components, and evaluate their relative performance as compared to traditional statistical approaches. They system is therefore simple, and is based on techniques for keywords extraction, and various classifier combinations including stacking and voting. TextCat participated in the Batch and Routing tasks. In the Batch task, it achieved a score of 39.02% normalized utility, and 26.37% F-measure respectively, averaged over all topics. The averaged uninterpolated precision for our best routing submission was 14.16%.

Physical Description:

6 p.

Language(s):
Subject(s):
Keyword(s): semantics | TextCat | classifiers | text filtering
Source: Eleventh Text Retrieval Conference (TREC), 2002, Gaithersburg, Maryland, United States
Contributor(s):
Partner:
UNT College of Engineering
Collection:
UNT Scholarly Works
Identifier:
  • ARK: ark:/67531/metadc30942
Resource Type: Paper
Format: Text
Rights:
Access: Public