Proceedings - Österreichische Gesellschaft für Artificial Intelligence

0.7
0.7
0.6
0.6
0.5
0.5
F-measure
0.4
0.3
F-measure
0.4
0.2
0.3
0.1
IG
CRF weights mean
CRF mixture weight
0
10 1 10 2 10 3 10 4 10 5 10 6
# of features
0.2
IG
CRF weights mean
CRF mixture weight
0.1
10 1 10 2 10 3 10 4 10 5 10 6
# of features
(a) bc-msnbc
(b) bn-cnn
0.8
0.7
0.7
0.6
0.6
0.5
F-measure
0.5
0.4
F-measure
0.4
0.3
0.3
0.2
IG
CRF weights mean
CRF mixture weight
0.1
10 1 10 2 10 3 10 4 10 5 10 6
# of features
0.2
IG
CRF weights mean
CRF mixture weight
0.1
10 1 10 2 10 3 10 4 10 5 10 6
# of features
(c) mz-sinorama
(d) nw-xinhua
0.7
0.6
0.5
F-measure
0.4
0.3
0.2
IG
CRF weights mean
CRF mixture weight
0.1
10 1 10 2 10 3 10 4 10 5 10 6
# of features
(e) wb-eng
Figure 1: Feature selection on different training data. The test data is mz-sinorama. The Y-axis on
all charts is F 1 -measure. The X-axis is the number of features. Lines with crosses stand for feature
selection based on information gain (IG). Lines with stars stand for feature selection based on the
feature weight in a mixture of domains (CRF mixture weight). Lines with circles stand for feature
selection based on the feature weights mean (CRF weights mean). One can see that the latter lines start
at higher values of F 1 and most of the time reach flat part of the chart faster than the lines corresponding
to other methods.
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Proceedings of KONVENS 2012 (Main track: poster presentations), Vienna, September 20, 2012