TABLE 6:

Multi-label Learning with Context

C	0	1	2	3	4	0	1	2	3	4
	ML					ML-MT
MISC
FA	0.903	0.918	0.912	0.918	0.917	0.911	0.918	0.918	0.917	0.919
GI	0.743	0.762	0.756	0.770	0.764	0.760	0.775	0.774	0.761	0.776
QUC	0.598	0.648	0.634	0.653	0.659	0.659	0.656	0.667	0.638	0.686
QUO	0.787	0.803	0.801	0.809	0.809	0.801	0.809	0.812	0.812	0.806
REC	0.504	0.549	0.560	0.558	0.594	0.564	0.592	0.576	0.572	0.570
RES	0.429	0.463	0.461	0.495	0.504	0.486	0.519	0.504	0.499	0.516
MIA	0.548	0.565	0.532	0.570	0.558	0.576	0.580	0.587	0.551	0.581
MIN	0.199	0.213	0.191	0.224	0.220	0.235	0.223	0.221	0.229	0.208
FN	0.949	0.956	0.956	0.960	0.954	0.958	0.959	0.956	0.960	0.960
POS	0.379	0.405	0.371	0.408	0.401	0.381	0.396	0.416	0.332	0.397
NEG	0.339	0.372	0.361	0.365	0.384	0.354	0.377	0.372	0.383	0.391
AVG	0.580	0.605	0.594	0.612	0.615	0.608	0.619	0.619	0.605	0.619
CTRS
AG	0.784	0.771	0.787	0.732	0.766	0.790	0.739	0.772	0.771	0.741
AT	0.714	0.733	0.749	0.691	0.739	0.731	0.712	0.739	0.742	0.707
CO	0.778	0.787	0.792	0.790	0.789	0.776	0.775	0.777	0.783	0.774
FB	0.751	0.750	0.770	0.716	0.754	0.772	0.753	0.778	0.753	0.712
GD	0.693	0.741	0.772	0.758	0.780	0.752	0.771	0.770	0.746	0.764
HW	0.743	0.705	0.731	0.637	0.737	0.654	0.643	0.723	0.735	0.703
IP	0.929	0.929	0.929	0.929	0.929	0.929	0.929	0.929	0.929	0.929
KC	0.717	0.736	0.765	0.722	0.743	0.753	0.753	0.757	0.726	0.726
PT	0.741	0.767	0.779	0.780	0.792	0.798	0.797	0.828	0.800	0.794
SC	0.695	0.726	0.746	0.695	0.743	0.744	0.737	0.752	0.702	0.718
UN	0.800	0.800	0.800	0.800	0.803	0.800	0.800	0.800	0.800	0.800
AVG	0.758	0.768	0.784	0.750	0.780	0.773	0.765	0.784	0.772	0.761