BioASQ Participants Area

Task 10b: Test Results of Phase A

* The "-" replace the scores of systems that didn't submit the corresponding annotations. *
The test results are presented in separate tables for each type of annotation. They are sorted based on the scores of MAP. The "System Description" of each system is used.
The evaluation measures that are used are presented here .

Test batch 1

Documents

System	Mean precision	Recall	F-Measure	MAP	GMAP
The basic end-to-end	0.1775	0.5185	0.2347	0.3849	0.0105
AUEB-System1	0.0822	0.3694	0.1241	0.2798	0.0018
ELECTROLBERT-0	0.0456	0.1569	0.0622	0.1121	0.0001
bio-answerfinder	0.2709	0.4005	0.2986	0.3541	0.0029
gsl_zs_hybrid	0.1244	0.5419	0.1827	0.4154	0.0234
bio-answerfinder-2	0.1296	0.4511	0.1806	0.3434	0.0070
gsl_zs_rrf1	0.1300	0.5592	0.1911	0.4292	0.0223
gsl_zs_rrf2	0.1278	0.5463	0.1877	0.4280	0.0202
gsl_zs_nn	0.1156	0.5193	0.1705	0.4126	0.0166
gsl_zs_rrf3	0.1244	0.5113	0.1814	0.3858	0.0148
Basic e2e mid speed	0.1697	0.4977	0.2240	0.3771	0.0077
RYGH-1	0.1330	0.5730	0.1958	0.4536	0.0262
RYGH	0.1430	0.5819	0.2065	0.4567	0.0277
RYGH-3	0.1421	0.6055	0.2080	0.4770	0.0438
RYGH-4	0.1407	0.6058	0.2065	0.4805	0.0440
AUEB-System2	0.1022	0.4373	0.1526	0.3329	0.0035
simple baseline solr	0.0011	0.0009	0.0010	0.0003	0.0000
bioinfo-0	0.1267	0.5521	0.1870	0.4098	0.0239
bioinfo-1	0.1244	0.5490	0.1845	0.4130	0.0215
bioinfo-2	0.1256	0.5512	0.1859	0.4059	0.0234
bioinfo-3	0.1233	0.5417	0.1824	0.4042	0.0202
ELECTROLBERT-1	0.0422	0.1534	0.0582	0.1082	0.0001
ELECTROLBERT-2	0.0422	0.1534	0.0582	0.1082	0.0001

Snippets

System	Mean precision	Recall	F-Measure	MAP	GMAP
The basic end-to-end	0.0911	0.0010	0.0019	0.1808	0.0006
AUEB-System1	0.0693	0.2117	0.0968	0.2495	0.0010
ELECTROLBERT-0	-	-	-	-	-
bio-answerfinder	0.1108	0.2750	0.1401	0.4927	0.0014
gsl_zs_hybrid	-	-	-	-	-
bio-answerfinder-2	0.0764	0.2276	0.1027	0.2431	0.0020
gsl_zs_rrf1	-	-	-	-	-
gsl_zs_rrf2	-	-	-	-	-
gsl_zs_nn	-	-	-	-	-
gsl_zs_rrf3	-	-	-	-	-
Basic e2e mid speed	0.0888	0.0009	0.0018	0.1798	0.0005
RYGH-1	0.1183	0.3762	0.1634	0.4021	0.0116
RYGH	0.1225	0.3883	0.1675	0.4226	0.0175
RYGH-3	0.1230	0.4299	0.1733	0.4375	0.0273
RYGH-4	0.1241	0.4244	0.1736	0.4299	0.0269
AUEB-System2	0.0790	0.2575	0.1117	0.2877	0.0019
simple baseline solr	-	-	-	-	-
bioinfo-0	-	-	-	-	-
bioinfo-1	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
ELECTROLBERT-1	-	-	-	-	-
ELECTROLBERT-2	-	-	-	-	-

Concepts

System	Mean precision	Recall	F-Measure	MAP	GMAP
The basic end-to-end	-	-	-	-	-
AUEB-System1	-	-	-	-	-
ELECTROLBERT-0	-	-	-	-	-
bio-answerfinder	-	-	-	-	-
gsl_zs_hybrid	-	-	-	-	-
bio-answerfinder-2	-	-	-	-	-
gsl_zs_rrf1	-	-	-	-	-
gsl_zs_rrf2	-	-	-	-	-
gsl_zs_nn	-	-	-	-	-
gsl_zs_rrf3	-	-	-	-	-
Basic e2e mid speed	-	-	-	-	-
RYGH-1	-	-	-	-	-
RYGH	-	-	-	-	-
RYGH-3	-	-	-	-	-
RYGH-4	-	-	-	-	-
AUEB-System2	-	-	-	-	-
simple baseline solr	-	-	-	-	-
bioinfo-0	-	-	-	-	-
bioinfo-1	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
ELECTROLBERT-1	-	-	-	-	-
ELECTROLBERT-2	-	-	-	-	-

RDF Triples

System	Mean precision	Recall	F-Measure	MAP	GMAP
The basic end-to-end	-	-	-	-	-
AUEB-System1	-	-	-	-	-
ELECTROLBERT-0	-	-	-	-	-
bio-answerfinder	-	-	-	-	-
gsl_zs_hybrid	-	-	-	-	-
bio-answerfinder-2	-	-	-	-	-
gsl_zs_rrf1	-	-	-	-	-
gsl_zs_rrf2	-	-	-	-	-
gsl_zs_nn	-	-	-	-	-
gsl_zs_rrf3	-	-	-	-	-
Basic e2e mid speed	-	-	-	-	-
RYGH-1	-	-	-	-	-
RYGH	-	-	-	-	-
RYGH-3	-	-	-	-	-
RYGH-4	-	-	-	-	-
AUEB-System2	-	-	-	-	-
simple baseline solr	-	-	-	-	-
bioinfo-0	-	-	-	-	-
bioinfo-1	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
ELECTROLBERT-1	-	-	-	-	-
ELECTROLBERT-2	-	-	-	-	-

Test batch 2

Documents

System	Mean precision	Recall	F-Measure	MAP	GMAP
simple baseline solr	0.0456	0.2403	0.0715	0.1409	0.0003
bio-answerfinder	0.2015	0.3793	0.2378	0.2989	0.0027
bio-answerfinder-2	0.1266	0.3668	0.1678	0.2751	0.0026
bio-answerfinder-3	0.1523	0.2804	0.1750	0.2104	0.0009
bio-answerfinder-4	0.1223	0.3502	0.1610	0.2661	0.0023
ELECTROLBERT-0	0.0456	0.2521	0.0716	0.1632	0.0003
AUEB-System1	0.0633	0.3195	0.0980	0.2276	0.0009
AUEB-System2	0.0900	0.4474	0.1389	0.3147	0.0048
The basic end-to-end	0.1203	0.4353	0.1669	0.2904	0.0042
Basic e2e mid speed	0.1192	0.4350	0.1657	0.2892	0.0045
bioinfo-0	0.1022	0.4572	0.1543	0.3186	0.0106
bioinfo-1	0.1022	0.4572	0.1543	0.3223	0.0104
gsl_zs_rrf1	0.1044	0.4854	0.1585	0.3572	0.0104
gsl_zs_hybrid	0.1011	0.4877	0.1547	0.3666	0.0097
gsl_zs_nn	0.0967	0.4808	0.1493	0.3453	0.0095
gsl_zs_rrf3	0.0967	0.4601	0.1477	0.3629	0.0065
gsl_zs_rrf2	0.0978	0.4824	0.1503	0.3647	0.0100
RYGH-5	0.0933	0.4470	0.1430	0.3427	0.0077
RYGH-1	0.1235	0.5559	0.1862	0.3977	0.0166
RYGH-3	0.1179	0.5275	0.1779	0.3855	0.0140
RYGH	0.1135	0.5194	0.1720	0.3795	0.0135
RYGH-4	0.1167	0.5266	0.1764	0.3839	0.0117
ELECTROLBERT-1	0.0400	0.2190	0.0626	0.1496	0.0002
ELECTROLBERT-2	0.0356	0.1882	0.0556	0.1075	0.0002
bioinfo-2	0.1033	0.4599	0.1558	0.3339	0.0099
bioinfo-3	0.1011	0.4649	0.1532	0.3199	0.0098
bioinfo-4	0.0878	0.4009	0.1331	0.2813	0.0041
Deep ML methods for	0.0800	0.4104	0.1242	0.2710	0.0034
MindLab QA System	0.0800	0.4104	0.1242	0.2710	0.0034
ELECTROLBERT-3	0.0378	0.2180	0.0606	0.1197	0.0002
MindLab Red Lions++	0.0800	0.4104	0.1242	0.2710	0.0034

Snippets

System	Mean precision	Recall	F-Measure	MAP	GMAP
simple baseline solr	-	-	-	-	-
bio-answerfinder	0.0977	0.2238	0.1165	0.3712	0.0010
bio-answerfinder-2	0.0822	0.2311	0.1062	0.2947	0.0013
bio-answerfinder-3	0.0846	0.1820	0.0987	0.3019	0.0005
bio-answerfinder-4	0.0782	0.2110	0.1001	0.2791	0.0011
ELECTROLBERT-0	-	-	-	-	-
AUEB-System1	0.0559	0.1352	0.0695	0.1914	0.0003
AUEB-System2	0.0672	0.1846	0.0850	0.2251	0.0009
The basic end-to-end	0.0555	0.0004	0.0009	0.0795	0.0001
Basic e2e mid speed	0.0564	0.0004	0.0009	0.0787	0.0001
bioinfo-0	-	-	-	-	-
bioinfo-1	-	-	-	-	-
gsl_zs_rrf1	-	-	-	-	-
gsl_zs_hybrid	-	-	-	-	-
gsl_zs_nn	-	-	-	-	-
gsl_zs_rrf3	-	-	-	-	-
gsl_zs_rrf2	-	-	-	-	-
RYGH-5	0.1002	0.3063	0.1404	0.6069	0.0023
RYGH-1	0.1147	0.3363	0.1519	0.4364	0.0050
RYGH-3	0.1078	0.3161	0.1427	0.4000	0.0043
RYGH	0.1073	0.3175	0.1433	0.4234	0.0048
RYGH-4	0.1098	0.3196	0.1455	0.4028	0.0041
ELECTROLBERT-1	-	-	-	-	-
ELECTROLBERT-2	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
bioinfo-4	-	-	-	-	-
Deep ML methods for	0.0645	0.1615	0.0820	0.3043	0.0001
MindLab QA System	0.0645	0.1615	0.0820	0.3043	0.0001
ELECTROLBERT-3	-	-	-	-	-
MindLab Red Lions++	0.0645	0.1615	0.0820	0.3043	0.0001

Concepts

System	Mean precision	Recall	F-Measure	MAP	GMAP
simple baseline solr	-	-	-	-	-
bio-answerfinder	-	-	-	-	-
bio-answerfinder-2	-	-	-	-	-
bio-answerfinder-3	-	-	-	-	-
bio-answerfinder-4	-	-	-	-	-
ELECTROLBERT-0	-	-	-	-	-
AUEB-System1	-	-	-	-	-
AUEB-System2	-	-	-	-	-
The basic end-to-end	-	-	-	-	-
Basic e2e mid speed	-	-	-	-	-
bioinfo-0	-	-	-	-	-
bioinfo-1	-	-	-	-	-
gsl_zs_rrf1	-	-	-	-	-
gsl_zs_hybrid	-	-	-	-	-
gsl_zs_nn	-	-	-	-	-
gsl_zs_rrf3	-	-	-	-	-
gsl_zs_rrf2	-	-	-	-	-
RYGH-5	-	-	-	-	-
RYGH-1	-	-	-	-	-
RYGH-3	-	-	-	-	-
RYGH	-	-	-	-	-
RYGH-4	-	-	-	-	-
ELECTROLBERT-1	-	-	-	-	-
ELECTROLBERT-2	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
bioinfo-4	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
MindLab QA System	-	-	-	-	-
ELECTROLBERT-3	-	-	-	-	-
MindLab Red Lions++	-	-	-	-	-

RDF Triples

System	Mean precision	Recall	F-Measure	MAP	GMAP
simple baseline solr	-	-	-	-	-
bio-answerfinder	-	-	-	-	-
bio-answerfinder-2	-	-	-	-	-
bio-answerfinder-3	-	-	-	-	-
bio-answerfinder-4	-	-	-	-	-
ELECTROLBERT-0	-	-	-	-	-
AUEB-System1	-	-	-	-	-
AUEB-System2	-	-	-	-	-
The basic end-to-end	-	-	-	-	-
Basic e2e mid speed	-	-	-	-	-
bioinfo-0	-	-	-	-	-
bioinfo-1	-	-	-	-	-
gsl_zs_rrf1	-	-	-	-	-
gsl_zs_hybrid	-	-	-	-	-
gsl_zs_nn	-	-	-	-	-
gsl_zs_rrf3	-	-	-	-	-
gsl_zs_rrf2	-	-	-	-	-
RYGH-5	-	-	-	-	-
RYGH-1	-	-	-	-	-
RYGH-3	-	-	-	-	-
RYGH	-	-	-	-	-
RYGH-4	-	-	-	-	-
ELECTROLBERT-1	-	-	-	-	-
ELECTROLBERT-2	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
bioinfo-4	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
MindLab QA System	-	-	-	-	-
ELECTROLBERT-3	-	-	-	-	-
MindLab Red Lions++	-	-	-	-	-

Test batch 3

Documents

System	Mean precision	Recall	F-Measure	MAP	GMAP
Basic e2e mid speed	0.1794	0.4891	0.2357	0.3733	0.0075
bio-answerfinder	0.2584	0.4470	0.2790	0.3581	0.0041
bio-answerfinder-2	0.1423	0.4572	0.1877	0.3415	0.0052
ELECTROLBERT-0	0.0600	0.3549	0.0959	0.2376	0.0011
ELECTROLBERT-1	0.0489	0.3043	0.0794	0.2091	0.0005
AUEB-System1	0.0956	0.4506	0.1394	0.3227	0.0036
AUEB-System2	0.1111	0.5243	0.1628	0.4227	0.0104
bio-answerfinder-3	0.2241	0.4100	0.2439	0.3420	0.0029
bio-answerfinder-4	0.1435	0.4683	0.1905	0.3474	0.0058
Deep ML methods for	0.0633	0.3424	0.0993	0.2068	0.0013
gsl_zs_hybrid	0.1122	0.5676	0.1729	0.4256	0.0172
gsl_zs_rrf1	0.1144	0.5763	0.1764	0.4304	0.0175
gsl_zs_nn	0.1033	0.5209	0.1591	0.4050	0.0100
gsl_zs_rrf3	0.1033	0.5331	0.1600	0.4087	0.0111
gsl_zs_rrf2	0.0922	0.4947	0.1439	0.3834	0.0077
RYGH-1	0.1280	0.6196	0.1965	0.4698	0.0234
RYGH	0.1267	0.6287	0.1956	0.4903	0.0200
RYGH-3	0.1244	0.6123	0.1916	0.4849	0.0217
RYGH-4	0.1251	0.6234	0.1931	0.4872	0.0242
RYGH-5	0.1256	0.6333	0.1946	0.5063	0.0256
bioinfo-0	0.1278	0.6302	0.1959	0.4352	0.0282
bioinfo-1	0.1278	0.6302	0.1959	0.4290	0.0260
bioinfo-2	0.1278	0.6302	0.1959	0.4290	0.0260
bioinfo-3	0.1200	0.5940	0.1843	0.4174	0.0186
bioinfo-4	0.1289	0.6321	0.1973	0.4279	0.0256
ELECTROLBERT-2	0.0889	0.4619	0.1377	0.3209	0.0044
ELECTROLBERT-3	0.0767	0.4090	0.1197	0.2854	0.0019
The basic end-to-end	0.1796	0.4891	0.2360	0.3753	0.0076
BioNIR Prepro-mid	0.1840	0.4851	0.2384	0.3639	0.0074

Snippets

System	Mean precision	Recall	F-Measure	MAP	GMAP
Basic e2e mid speed	0.1170	0.3329	0.1559	0.4463	0.0039
bio-answerfinder	0.1270	0.3252	0.1617	0.5445	0.0029
bio-answerfinder-2	0.0984	0.2726	0.1300	0.3249	0.0027
ELECTROLBERT-0	-	-	-	-	-
ELECTROLBERT-1	-	-	-	-	-
AUEB-System1	0.0776	0.2382	0.1062	0.3266	0.0019
AUEB-System2	0.0885	0.2949	0.1230	0.3953	0.0048
bio-answerfinder-3	0.1171	0.2952	0.1506	0.4699	0.0023
bio-answerfinder-4	0.0986	0.2833	0.1309	0.3306	0.0030
Deep ML methods for	0.0238	0.0970	0.0344	0.0966	0.0001
gsl_zs_hybrid	-	-	-	-	-
gsl_zs_rrf1	-	-	-	-	-
gsl_zs_nn	-	-	-	-	-
gsl_zs_rrf3	-	-	-	-	-
gsl_zs_rrf2	-	-	-	-	-
RYGH-1	0.1227	0.4234	0.1742	0.4935	0.0106
RYGH	0.1230	0.4420	0.1760	0.4946	0.0115
RYGH-3	0.1212	0.4202	0.1721	0.4792	0.0097
RYGH-4	0.1229	0.4328	0.1746	0.4858	0.0106
RYGH-5	0.1546	0.4721	0.2125	0.8305	0.0143
bioinfo-0	-	-	-	-	-
bioinfo-1	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
bioinfo-4	-	-	-	-	-
ELECTROLBERT-2	-	-	-	-	-
ELECTROLBERT-3	-	-	-	-	-
The basic end-to-end	0.1163	0.3329	0.1554	0.4486	0.0039
BioNIR Prepro-mid	0.1144	0.3208	0.1503	0.4309	0.0040

Concepts

System	Mean precision	Recall	F-Measure	MAP	GMAP
Basic e2e mid speed	-	-	-	-	-
bio-answerfinder	-	-	-	-	-
bio-answerfinder-2	-	-	-	-	-
ELECTROLBERT-0	-	-	-	-	-
ELECTROLBERT-1	-	-	-	-	-
AUEB-System1	-	-	-	-	-
AUEB-System2	-	-	-	-	-
bio-answerfinder-3	-	-	-	-	-
bio-answerfinder-4	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
gsl_zs_hybrid	-	-	-	-	-
gsl_zs_rrf1	-	-	-	-	-
gsl_zs_nn	-	-	-	-	-
gsl_zs_rrf3	-	-	-	-	-
gsl_zs_rrf2	-	-	-	-	-
RYGH-1	-	-	-	-	-
RYGH	-	-	-	-	-
RYGH-3	-	-	-	-	-
RYGH-4	-	-	-	-	-
RYGH-5	-	-	-	-	-
bioinfo-0	-	-	-	-	-
bioinfo-1	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
bioinfo-4	-	-	-	-	-
ELECTROLBERT-2	-	-	-	-	-
ELECTROLBERT-3	-	-	-	-	-
The basic end-to-end	-	-	-	-	-
BioNIR Prepro-mid	-	-	-	-	-

RDF Triples

System	Mean precision	Recall	F-Measure	MAP	GMAP
Basic e2e mid speed	-	-	-	-	-
bio-answerfinder	-	-	-	-	-
bio-answerfinder-2	-	-	-	-	-
ELECTROLBERT-0	-	-	-	-	-
ELECTROLBERT-1	-	-	-	-	-
AUEB-System1	-	-	-	-	-
AUEB-System2	-	-	-	-	-
bio-answerfinder-3	-	-	-	-	-
bio-answerfinder-4	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
gsl_zs_hybrid	-	-	-	-	-
gsl_zs_rrf1	-	-	-	-	-
gsl_zs_nn	-	-	-	-	-
gsl_zs_rrf3	-	-	-	-	-
gsl_zs_rrf2	-	-	-	-	-
RYGH-1	-	-	-	-	-
RYGH	-	-	-	-	-
RYGH-3	-	-	-	-	-
RYGH-4	-	-	-	-	-
RYGH-5	-	-	-	-	-
bioinfo-0	-	-	-	-	-
bioinfo-1	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
bioinfo-4	-	-	-	-	-
ELECTROLBERT-2	-	-	-	-	-
ELECTROLBERT-3	-	-	-	-	-
The basic end-to-end	-	-	-	-	-
BioNIR Prepro-mid	-	-	-	-	-

Test batch 4

Documents

System	Mean precision	Recall	F-Measure	MAP	GMAP
AUEB-System1	0.0800	0.3885	0.1238	0.2592	0.0017
AUEB-System2	0.0889	0.4070	0.1362	0.3021	0.0026
bio-answerfinder	0.2500	0.4180	0.2718	0.3217	0.0064
bio-answerfinder-2	0.1360	0.4335	0.1849	0.3034	0.0060
Basic e2e mid speed	0.1503	0.4069	0.1976	0.2924	0.0048
bio-answerfinder-3	0.2069	0.3817	0.2317	0.3106	0.0037
bio-answerfinder-4	0.1383	0.4469	0.1886	0.3056	0.0066
LaRSA	0.1152	0.4631	0.1703	0.3342	0.0073
The basic end-to-end	0.1432	0.3854	0.1881	0.2804	0.0034
ELECTROLBERT-2	0.1022	0.4889	0.1574	0.3101	0.0075
gsl_zs_hybrid	0.1011	0.5015	0.1573	0.3904	0.0084
gsl_zs_rrf1	0.0989	0.4960	0.1541	0.3829	0.0082
gsl_zs_rrf2	0.1011	0.5024	0.1574	0.3913	0.0083
gsl_zs_nn	0.0933	0.4700	0.1452	0.3609	0.0098
gsl_zs_rrf3	0.1000	0.4997	0.1558	0.3778	0.0089
Deep ML methods for	0.0556	0.2326	0.0834	0.1245	0.0004
MindLab QA System	0.0556	0.2326	0.0834	0.1245	0.0004
RYGH-1	0.1091	0.5496	0.1704	0.4040	0.0183
RYGH	0.1080	0.5381	0.1684	0.3925	0.0138
RYGH-4	0.1111	0.5424	0.1720	0.3883	0.0166
RYGH-5	0.1100	0.5387	0.1703	0.3873	0.0152
bioinfo-0	0.0989	0.4825	0.1528	0.3404	0.0077
bioinfo-1	0.1011	0.4783	0.1552	0.3502	0.0085
bioinfo-2	0.1011	0.4708	0.1549	0.3552	0.0083
bioinfo-3	0.1133	0.5116	0.1728	0.3613	0.0117
bioinfo-4	0.1000	0.4764	0.1539	0.3519	0.0085
RYGH-3	0.1091	0.5478	0.1703	0.4058	0.0169

Snippets

System	Mean precision	Recall	F-Measure	MAP	GMAP
AUEB-System1	0.0597	0.1597	0.0834	0.2480	0.0008
AUEB-System2	0.0657	0.1648	0.0905	0.2653	0.0007
bio-answerfinder	0.1270	0.2790	0.1619	0.4905	0.0047
bio-answerfinder-2	0.0878	0.2301	0.1182	0.2949	0.0031
Basic e2e mid speed	0.0887	0.2146	0.1184	0.3321	0.0019
bio-answerfinder-3	0.1114	0.2672	0.1463	0.4456	0.0031
bio-answerfinder-4	0.0887	0.2342	0.1197	0.2973	0.0031
LaRSA	0.0553	0.1312	0.0720	0.1393	0.0009
The basic end-to-end	0.0850	0.2089	0.1140	0.3225	0.0013
ELECTROLBERT-2	-	-	-	-	-
gsl_zs_hybrid	-	-	-	-	-
gsl_zs_rrf1	-	-	-	-	-
gsl_zs_rrf2	-	-	-	-	-
gsl_zs_nn	-	-	-	-	-
gsl_zs_rrf3	-	-	-	-	-
Deep ML methods for	0.0121	0.0365	0.0163	0.0363	0.0000
MindLab QA System	0.0121	0.0374	0.0163	0.0358	0.0000
RYGH-1	0.0845	0.2801	0.1235	0.3620	0.0059
RYGH	0.0836	0.2747	0.1215	0.3523	0.0049
RYGH-4	0.1119	0.3333	0.1577	0.6606	0.0036
RYGH-5	0.1126	0.3292	0.1578	0.6596	0.0036
bioinfo-0	-	-	-	-	-
bioinfo-1	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
bioinfo-4	-	-	-	-	-
RYGH-3	0.0859	0.2862	0.1257	0.3669	0.0067

Concepts

System	Mean precision	Recall	F-Measure	MAP	GMAP
AUEB-System1	-	-	-	-	-
AUEB-System2	-	-	-	-	-
bio-answerfinder	-	-	-	-	-
bio-answerfinder-2	-	-	-	-	-
Basic e2e mid speed	-	-	-	-	-
bio-answerfinder-3	-	-	-	-	-
bio-answerfinder-4	-	-	-	-	-
LaRSA	-	-	-	-	-
The basic end-to-end	-	-	-	-	-
ELECTROLBERT-2	-	-	-	-	-
gsl_zs_hybrid	-	-	-	-	-
gsl_zs_rrf1	-	-	-	-	-
gsl_zs_rrf2	-	-	-	-	-
gsl_zs_nn	-	-	-	-	-
gsl_zs_rrf3	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
MindLab QA System	-	-	-	-	-
RYGH-1	-	-	-	-	-
RYGH	-	-	-	-	-
RYGH-4	-	-	-	-	-
RYGH-5	-	-	-	-	-
bioinfo-0	-	-	-	-	-
bioinfo-1	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
bioinfo-4	-	-	-	-	-
RYGH-3	-	-	-	-	-

RDF Triples

System	Mean precision	Recall	F-Measure	MAP	GMAP
AUEB-System1	-	-	-	-	-
AUEB-System2	-	-	-	-	-
bio-answerfinder	-	-	-	-	-
bio-answerfinder-2	-	-	-	-	-
Basic e2e mid speed	-	-	-	-	-
bio-answerfinder-3	-	-	-	-	-
bio-answerfinder-4	-	-	-	-	-
LaRSA	-	-	-	-	-
The basic end-to-end	-	-	-	-	-
ELECTROLBERT-2	-	-	-	-	-
gsl_zs_hybrid	-	-	-	-	-
gsl_zs_rrf1	-	-	-	-	-
gsl_zs_rrf2	-	-	-	-	-
gsl_zs_nn	-	-	-	-	-
gsl_zs_rrf3	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
MindLab QA System	-	-	-	-	-
RYGH-1	-	-	-	-	-
RYGH	-	-	-	-	-
RYGH-4	-	-	-	-	-
RYGH-5	-	-	-	-	-
bioinfo-0	-	-	-	-	-
bioinfo-1	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
bioinfo-4	-	-	-	-	-
RYGH-3	-	-	-	-	-

Test batch 5

Documents

System	Mean precision	Recall	F-Measure	MAP	GMAP
bio-answerfinder	0.2912	0.4205	0.3160	0.3297	0.0044
bio-answerfinder-2	0.1355	0.3603	0.1751	0.2722	0.0034
AUEB-System1	0.0833	0.3543	0.1183	0.2636	0.0015
AUEB-System2	0.1022	0.4078	0.1447	0.3071	0.0037
bio-answerfinder-3	0.2035	0.3134	0.2199	0.2475	0.0011
bio-answerfinder-4	0.1284	0.3429	0.1649	0.2548	0.0022
ELECTROLBERT-2	0.0833	0.4148	0.1299	0.3029	0.0041
LaRSA	0.0878	0.4535	0.1371	0.3078	0.0055
gsl_zs_hybrid	0.1033	0.4773	0.1584	0.3687	0.0076
gsl_zs_rrf1	0.1000	0.4706	0.1540	0.3757	0.0076
gsl_zs_nn	0.0944	0.4461	0.1455	0.3572	0.0058
gsl_zs_rrf3	0.0989	0.4579	0.1518	0.3588	0.0059
RYGH	0.1140	0.5096	0.1732	0.3997	0.0087
RYGH-3	0.1028	0.4876	0.1576	0.3985	0.0078
RYGH-5	0.1000	0.4826	0.1536	0.3846	0.0081
RYGH-1	0.1040	0.4904	0.1592	0.3968	0.0078
RYGH-4	0.1100	0.5161	0.1682	0.4154	0.0109
ELECTROLBERT-3	0.0811	0.4122	0.1263	0.3242	0.0036
ELECTROLBERT-1	0.0800	0.4011	0.1243	0.3230	0.0032
gsl_zs_rrf2	0.1000	0.4627	0.1534	0.3623	0.0060
bioinfo-0	0.1056	0.4968	0.1619	0.3734	0.0108
bioinfo-1	0.1044	0.4932	0.1605	0.3706	0.0109
bioinfo-2	0.1067	0.4993	0.1634	0.3704	0.0113
bioinfo-3	0.1033	0.4892	0.1584	0.3718	0.0087
bioinfo-4	0.1022	0.4928	0.1581	0.3760	0.0111

Snippets

System	Mean precision	Recall	F-Measure	MAP	GMAP