Table 9

F-Measure results using all the features and all but one of the features

ALL

A

B

C

D

E

F

G

H

I

J

K


S1

OBJ

.90

.89

.87

.92

.90

.90

.91

.91

.91

.92

.91

.88

METH

.80

.81

.80

.80

.79

.81

.79

.80

.80

.80

.81

.81

RES

.88

.90

.88

.90

.88

.90

.88

.88

.88

.89

.89

.90

CON

.86

.85

.82

.87

.88

.90

.90

.88

.89

.88

.88

.90


S2

BKG

.91

.94

.90

.90

.93

.94

.94

.91

.93

.94

.92

.94

OBJ

.72

.78

.84

.78

.83

.88

.84

.81

.83

.84

.78

.83

METH

.81

.83

.80

.81

.80

.85

.80

.78

.81

.81

.82

.83

RES

.88

.90

.88

.89

.88

.91

.89

.89

.90

.90

.90

.89

CON

.84

.83

.77

.83

.86

.88

.86

.87

.88

.89

.88

.81

REL

-

-

-

-

-

-

-

-

-

-

-

-

FUT

-

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0

1.0


S3

HYP

-

-

-

-

-

-

-

-

-

-

-

-

MOT

.82

.84

.80

.76

.82

.82

.83

.78

.83

.83

.83

.83

BKG

.59

.60

.60

.54

.67

.62

.62

.59

.61

.61

.62

.61

GOAL

.62

.67

.67

.62

.71

.62

.67

.43

.67

.67

.67

.62

OBJT

.88

.85

.83

.74

.83

.85

.83

.74

.83

.83

.83

.85

EXP

.72

.68

.72

.53

.65

.70

.72

.73

.74

.74

.72

.68

MOD

-

-

-

-

-

-

-

-

-

-

-

-

METH

.87

.86

.87

.66

.85

.89

.87

.88

.86

.86

.87

.86

OBS

.82

.81

.84

.72

.80

.82

.81

.80

.82

.82

.81

.81

RES

.87

.87

.88

.74

.87

.86

.87

.86

.87

.87

.87

.88

CON

.88

.88

.82

.88

.83

.87

.87

.84

.87

.88

.87

.86


A-K: History, Location, Word, Bi-gram, Verb, Verb Class, POS, GR, Subj, Obj, Voice

We have 1.0 for FUT in S2 probably because the size of the training data is just right, and the model doesn't over-fit the data. We make this assumption because we have 1.0 for almost all the categories on the training data, but only for FUT on the test data.

Guo et al. BMC Bioinformatics 2011 12:69   doi:10.1186/1471-2105-12-69

Open Data