BioASQ Participants Area

Task 8b: 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
AUEB-System1	0.1720	0.4397	0.2028	0.3648	0.0114
AUEB-System3	0.1720	0.4397	0.2028	0.2747	0.0098
AUEB-System5	0.1490	0.3914	0.1762	0.3066	0.0068
pa	0.2822	0.4307	0.2977	0.3591	0.0109
AUEB-System4	0.0310	0.0821	0.0364	0.0547	0.0001
bio-answerfinder	0.2933	0.2841	0.2474	0.2405	0.0024
bioinfo-0	0.1630	0.4557	0.1981	0.3409	0.0091
bioinfo-1	0.1760	0.4684	0.2130	0.3745	0.0142
bioinfo-2	0.1740	0.4997	0.2146	0.3744	0.0173
bioinfo-3	0.1730	0.4972	0.2143	0.3798	0.0172
bioinfo-4	0.1790	0.4946	0.2187	0.3823	0.0163
Deep ML methods for	0.1130	0.2092	0.1221	0.1650	0.0007
MindLab QA System	0.1130	0.2092	0.1221	0.1650	0.0007
MindLab QA Reloaded	0.1130	0.2092	0.1221	0.1650	0.0007
MindLab QA System ++	0.1130	0.2092	0.1221	0.1650	0.0007
MindLab Red Lions++	0.1130	0.2092	0.1221	0.1650	0.0007
lalala	0.1650	0.4554	0.2018	0.3595	0.0094
Ir_sys1	0.1520	0.3909	0.1814	0.3362	0.0039
Ir_sys2	0.1560	0.4352	0.1902	0.3372	0.0075
Ir_sys3	0.1600	0.4393	0.1934	0.3380	0.0095
Ir_sys4	0.1590	0.4393	0.1935	0.3352	0.0084

Snippets

System	Mean precision	Recall	F-Measure	MAP	GMAP
AUEB-System1	0.2142	0.2562	0.1960	0.7703	0.0038
AUEB-System3	0.1183	0.1286	0.0947	0.2323	0.0016
AUEB-System5	0.1395	0.1475	0.1117	0.3273	0.0021
pa	0.2030	0.2635	0.1973	0.5441	0.0057
AUEB-System4	0.0242	0.0325	0.0192	0.0320	0.0000
bio-answerfinder	0.1773	0.1896	0.1466	0.4704	0.0015
bioinfo-0	-	-	-	-	-
bioinfo-1	0.2006	0.1911	0.1612	0.2690	0.0012
bioinfo-2	0.2231	0.1826	0.1696	0.2965	0.0013
bioinfo-3	0.2206	0.1880	0.1724	0.2313	0.0011
bioinfo-4	0.2259	0.1910	0.1704	0.2804	0.0017
Deep ML methods for	0.0822	0.0774	0.0725	0.1714	0.0002
MindLab QA System	0.0749	0.0880	0.0729	0.1000	0.0002
MindLab QA Reloaded	0.0852	0.0833	0.0764	0.1643	0.0002
MindLab QA System ++	0.0800	0.0757	0.0716	0.1792	0.0001
MindLab Red Lions++	0.0915	0.0789	0.0776	0.1530	0.0002
lalala	-	-	-	-	-
Ir_sys1	-	-	-	-	-
Ir_sys2	-	-	-	-	-
Ir_sys3	-	-	-	-	-
Ir_sys4	-	-	-	-	-

Concepts

System	Mean precision	Recall	F-Measure	MAP	GMAP
AUEB-System1	-	-	-	-	-
AUEB-System3	-	-	-	-	-
AUEB-System5	-	-	-	-	-
pa	-	-	-	-	-
AUEB-System4	-	-	-	-	-
bio-answerfinder	-	-	-	-	-
bioinfo-0	-	-	-	-	-
bioinfo-1	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
bioinfo-4	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
MindLab QA System	-	-	-	-	-
MindLab QA Reloaded	-	-	-	-	-
MindLab QA System ++	-	-	-	-	-
MindLab Red Lions++	-	-	-	-	-
lalala	-	-	-	-	-
Ir_sys1	-	-	-	-	-
Ir_sys2	-	-	-	-	-
Ir_sys3	-	-	-	-	-
Ir_sys4	-	-	-	-	-

RDF Triples

System	Mean precision	Recall	F-Measure	MAP	GMAP
AUEB-System1	-	-	-	-	-
AUEB-System3	-	-	-	-	-
AUEB-System5	-	-	-	-	-
pa	-	-	-	-	-
AUEB-System4	-	-	-	-	-
bio-answerfinder	-	-	-	-	-
bioinfo-0	-	-	-	-	-
bioinfo-1	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
bioinfo-4	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
MindLab QA System	-	-	-	-	-
MindLab QA Reloaded	-	-	-	-	-
MindLab QA System ++	-	-	-	-	-
MindLab Red Lions++	-	-	-	-	-
lalala	-	-	-	-	-
Ir_sys1	-	-	-	-	-
Ir_sys2	-	-	-	-	-
Ir_sys3	-	-	-	-	-
Ir_sys4	-	-	-	-	-

Test batch 2

Documents

System	Mean precision	Recall	F-Measure	MAP	GMAP
AUEB-System1	0.2448	0.5175	0.2742	0.3806	0.0522
AUEB-System2	0.2388	0.5115	0.2687	0.3632	0.0455
AUEB-System3	0.2448	0.5175	0.2742	0.3122	0.0416
AUEB-System4	0.0290	0.0837	0.0340	0.0723	0.0001
AUEB-System5	0.2198	0.4618	0.2438	0.3243	0.0307
Google-AdHoc-MAGLEV	0.1970	0.4623	0.2261	0.3192	0.0346
Google-AdHoc-Neural	0.1230	0.3246	0.1470	0.1799	0.0047
Google-AdHoc-BM25	0.1984	0.4540	0.2263	0.3054	0.0295
bio-answerfinder	0.2584	0.3367	0.2376	0.2497	0.0075
bioinfo-4	0.2310	0.5160	0.2615	0.3686	0.0417
bioinfo-1	0.2240	0.5104	0.2572	0.3707	0.0566
bioinfo-3	0.2240	0.5198	0.2581	0.3719	0.0675
bioinfo-2	0.2180	0.5004	0.2496	0.3643	0.0465
MindLab QA Reloaded	0.1420	0.2964	0.1566	0.1989	0.0055
MindLab Red Lions++	0.1420	0.2964	0.1566	0.1989	0.0055
Deep ML methods for	0.1420	0.2964	0.1566	0.1989	0.0055
MindLab QA System	0.1420	0.2964	0.1566	0.1989	0.0055
MindLab QA System ++	0.1420	0.2964	0.1566	0.1989	0.0055
pa	0.2848	0.4960	0.3013	0.3945	0.0600
Ir_sys1	0.1800	0.4154	0.2050	0.2663	0.0130
Ir_sys2	0.1670	0.4246	0.1973	0.3036	0.0120
bioinfo-0	0.2140	0.4754	0.2428	0.3411	0.0378
Ir_sys4	0.1670	0.4246	0.1973	0.3120	0.0129
lalala	0.1890	0.4338	0.2158	0.3091	0.0163
Google-AdHoc-M-Full	0.1520	0.3648	0.1762	0.2182	0.0087
Ir_sys3	0.1838	0.3944	0.2043	0.2913	0.0091

Snippets

System	Mean precision	Recall	F-Measure	MAP	GMAP
AUEB-System1	0.2257	0.2832	0.2191	0.7464	0.0089
AUEB-System2	0.1930	0.2435	0.1909	0.7058	0.0044
AUEB-System3	0.1415	0.1348	0.1076	0.1845	0.0022
AUEB-System4	0.0170	0.0103	0.0114	0.0306	0.0000
AUEB-System5	0.1380	0.1291	0.1050	0.2550	0.0022
Google-AdHoc-MAGLEV	-	-	-	-	-
Google-AdHoc-Neural	-	-	-	-	-
Google-AdHoc-BM25	-	-	-	-	-
bio-answerfinder	0.1594	0.1912	0.1410	0.1899	0.0036
bioinfo-4	0.1834	0.2181	0.1712	0.3051	0.0052
bioinfo-1	0.1839	0.2250	0.1723	0.2941	0.0063
bioinfo-3	0.1767	0.2128	0.1646	0.2889	0.0051
bioinfo-2	0.1758	0.2118	0.1651	0.2935	0.0039
MindLab QA Reloaded	0.0940	0.1247	0.0905	0.2410	0.0006
MindLab Red Lions++	0.1235	0.1606	0.1153	0.2783	0.0015
Deep ML methods for	0.0966	0.0948	0.0862	0.2115	0.0003
MindLab QA System	0.1185	0.1581	0.1125	0.2819	0.0015
MindLab QA System ++	0.0881	0.1118	0.0845	0.2077	0.0004
pa	0.2123	0.2943	0.2132	0.4147	0.0222
Ir_sys1	-	-	-	-	-
Ir_sys2	-	-	-	-	-
bioinfo-0	0.1722	0.1794	0.1492	0.2193	0.0031
Ir_sys4	-	-	-	-	-
lalala	-	-	-	-	-
Google-AdHoc-M-Full	-	-	-	-	-
Ir_sys3	-	-	-	-	-

Concepts

System	Mean precision	Recall	F-Measure	MAP	GMAP
AUEB-System1	-	-	-	-	-
AUEB-System2	-	-	-	-	-
AUEB-System3	-	-	-	-	-
AUEB-System4	-	-	-	-	-
AUEB-System5	-	-	-	-	-
Google-AdHoc-MAGLEV	-	-	-	-	-
Google-AdHoc-Neural	-	-	-	-	-
Google-AdHoc-BM25	-	-	-	-	-
bio-answerfinder	-	-	-	-	-
bioinfo-4	-	-	-	-	-
bioinfo-1	-	-	-	-	-
bioinfo-3	-	-	-	-	-
bioinfo-2	-	-	-	-	-
MindLab QA Reloaded	-	-	-	-	-
MindLab Red Lions++	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
MindLab QA System	-	-	-	-	-
MindLab QA System ++	-	-	-	-	-
pa	-	-	-	-	-
Ir_sys1	-	-	-	-	-
Ir_sys2	-	-	-	-	-
bioinfo-0	-	-	-	-	-
Ir_sys4	-	-	-	-	-
lalala	-	-	-	-	-
Google-AdHoc-M-Full	-	-	-	-	-
Ir_sys3	-	-	-	-	-

RDF Triples

System	Mean precision	Recall	F-Measure	MAP	GMAP
AUEB-System1	-	-	-	-	-
AUEB-System2	-	-	-	-	-
AUEB-System3	-	-	-	-	-
AUEB-System4	-	-	-	-	-
AUEB-System5	-	-	-	-	-
Google-AdHoc-MAGLEV	-	-	-	-	-
Google-AdHoc-Neural	-	-	-	-	-
Google-AdHoc-BM25	-	-	-	-	-
bio-answerfinder	-	-	-	-	-
bioinfo-4	-	-	-	-	-
bioinfo-1	-	-	-	-	-
bioinfo-3	-	-	-	-	-
bioinfo-2	-	-	-	-	-
MindLab QA Reloaded	-	-	-	-	-
MindLab Red Lions++	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
MindLab QA System	-	-	-	-	-
MindLab QA System ++	-	-	-	-	-
pa	-	-	-	-	-
Ir_sys1	-	-	-	-	-
Ir_sys2	-	-	-	-	-
bioinfo-0	-	-	-	-	-
Ir_sys4	-	-	-	-	-
lalala	-	-	-	-	-
Google-AdHoc-M-Full	-	-	-	-	-
Ir_sys3	-	-	-	-	-

Test batch 3

Documents

System	Mean precision	Recall	F-Measure	MAP	GMAP
Google-MAGLEV-RERANK	0.1920	0.5593	0.2316	0.5045	0.0512
Google-AdHoc-Neural	0.1280	0.4196	0.1604	0.2941	0.0078
Google-AdHoc-MAGLEV	0.1920	0.5593	0.2316	0.4564	0.0434
Google-AdHoc-BM25	0.2020	0.5510	0.2429	0.4460	0.0375
AUEB-System1	0.2283	0.5861	0.2654	0.5317	0.0621
AUEB-System2	0.2193	0.5772	0.2575	0.5215	0.0562
AUEB-System3	0.2283	0.5861	0.2654	0.3960	0.0483
AUEB-System4	0.2233	0.5802	0.2604	0.5329	0.0625
AUEB-System5	0.1973	0.5318	0.2328	0.4295	0.0496
bio-answerfinder	0.2950	0.3521	0.2740	0.3121	0.0046
Ir_sys1	0.1680	0.4593	0.2027	0.3482	0.0108
Ir_sys2	0.1580	0.4428	0.1900	0.3828	0.0100
Ir_sys3	0.1650	0.4546	0.1986	0.3798	0.0147
bioinfo-1	0.2070	0.5673	0.2496	0.5023	0.0610
bioinfo-2	0.2070	0.5762	0.2505	0.5101	0.0736
bioinfo-3	0.2110	0.5832	0.2542	0.5230	0.0763
bioinfo-4	0.2170	0.5829	0.2592	0.5108	0.0776
bioinfo-0	0.2130	0.5848	0.2563	0.5172	0.0773
MindLab QA System	0.1830	0.5097	0.2191	0.3938	0.0188
MindLab QA System ++	0.1830	0.5097	0.2191	0.3938	0.0188
MindLab Red Lions++	0.1830	0.5097	0.2191	0.3938	0.0188
MindLab QA Reloaded	0.1400	0.3876	0.1692	0.2872	0.0064
Deep ML methods for	0.1820	0.5094	0.2185	0.3940	0.0188
pa	0.3662	0.5671	0.3919	0.5273	0.0606
pa-base	0.3662	0.5671	0.3919	0.5273	0.0606
Google-AdHoc-M-Full	0.1920	0.5593	0.2316	0.4564	0.0434
Ir_sys4	0.1680	0.4655	0.2019	0.3912	0.0125
lalala	0.1780	0.4793	0.2124	0.4007	0.0177

Snippets

System	Mean precision	Recall	F-Measure	MAP	GMAP
Google-MAGLEV-RERANK	-	-	-	-	-
Google-AdHoc-Neural	-	-	-	-	-
Google-AdHoc-MAGLEV	-	-	-	-	-
Google-AdHoc-BM25	-	-	-	-	-
AUEB-System1	0.2654	0.4026	0.2626	1.1262	0.0349
AUEB-System2	0.2591	0.4097	0.2657	1.1547	0.0349
AUEB-System3	0.1482	0.1811	0.1285	0.2704	0.0065
AUEB-System4	0.2096	0.2854	0.1862	0.4821	0.0239
AUEB-System5	0.1649	0.2057	0.1430	0.3653	0.0056
bio-answerfinder	0.1928	0.2174	0.1573	0.3649	0.0027
Ir_sys1	-	-	-	-	-
Ir_sys2	-	-	-	-	-
Ir_sys3	-	-	-	-	-
bioinfo-1	0.2240	0.3199	0.2151	0.4528	0.0170
bioinfo-2	0.2278	0.3341	0.2229	0.4704	0.0162
bioinfo-3	0.2205	0.3481	0.2252	0.4864	0.0170
bioinfo-4	0.2265	0.3203	0.2179	0.4474	0.0152
bioinfo-0	-	-	-	-	-
MindLab QA System	0.1284	0.1799	0.1239	0.3561	0.0013
MindLab QA System ++	0.1280	0.1861	0.1222	0.2560	0.0022
MindLab Red Lions++	0.1333	0.1855	0.1269	0.2619	0.0022
MindLab QA Reloaded	0.0871	0.1408	0.0882	0.1999	0.0006
Deep ML methods for	0.0643	0.0582	0.0512	0.0584	0.0002
pa	0.2922	0.3887	0.2774	0.7946	0.0372
pa-base	0.2613	0.3774	0.2583	0.8051	0.0237
Google-AdHoc-M-Full	-	-	-	-	-
Ir_sys4	-	-	-	-	-
lalala	-	-	-	-	-

Concepts

System	Mean precision	Recall	F-Measure	MAP	GMAP
Google-MAGLEV-RERANK	-	-	-	-	-
Google-AdHoc-Neural	-	-	-	-	-
Google-AdHoc-MAGLEV	-	-	-	-	-
Google-AdHoc-BM25	-	-	-	-	-
AUEB-System1	-	-	-	-	-
AUEB-System2	-	-	-	-	-
AUEB-System3	-	-	-	-	-
AUEB-System4	-	-	-	-	-
AUEB-System5	-	-	-	-	-
bio-answerfinder	-	-	-	-	-
Ir_sys1	-	-	-	-	-
Ir_sys2	-	-	-	-	-
Ir_sys3	-	-	-	-	-
bioinfo-1	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
bioinfo-4	-	-	-	-	-
bioinfo-0	-	-	-	-	-
MindLab QA System	-	-	-	-	-
MindLab QA System ++	-	-	-	-	-
MindLab Red Lions++	-	-	-	-	-
MindLab QA Reloaded	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
pa	-	-	-	-	-
pa-base	-	-	-	-	-
Google-AdHoc-M-Full	-	-	-	-	-
Ir_sys4	-	-	-	-	-
lalala	-	-	-	-	-

RDF Triples

System	Mean precision	Recall	F-Measure	MAP	GMAP
Google-MAGLEV-RERANK	-	-	-	-	-
Google-AdHoc-Neural	-	-	-	-	-
Google-AdHoc-MAGLEV	-	-	-	-	-
Google-AdHoc-BM25	-	-	-	-	-
AUEB-System1	-	-	-	-	-
AUEB-System2	-	-	-	-	-
AUEB-System3	-	-	-	-	-
AUEB-System4	-	-	-	-	-
AUEB-System5	-	-	-	-	-
bio-answerfinder	-	-	-	-	-
Ir_sys1	-	-	-	-	-
Ir_sys2	-	-	-	-	-
Ir_sys3	-	-	-	-	-
bioinfo-1	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
bioinfo-4	-	-	-	-	-
bioinfo-0	-	-	-	-	-
MindLab QA System	-	-	-	-	-
MindLab QA System ++	-	-	-	-	-
MindLab Red Lions++	-	-	-	-	-
MindLab QA Reloaded	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
pa	-	-	-	-	-
pa-base	-	-	-	-	-
Google-AdHoc-M-Full	-	-	-	-	-
Ir_sys4	-	-	-	-	-
lalala	-	-	-	-	-

Test batch 4

Documents

System	Mean precision	Recall	F-Measure	MAP	GMAP
AUEB-System1	0.2380	0.5941	0.2793	0.4901	0.0686
AUEB-System2	0.2300	0.5886	0.2726	0.4945	0.0692
AUEB-System3	0.2380	0.5941	0.2793	0.3605	0.0523
AUEB-System4	0.2370	0.5941	0.2787	0.4992	0.0700
AUEB-System5	0.1990	0.5157	0.2368	0.4076	0.0271
Google-MAGLEV-RERANK	0.2030	0.5644	0.2471	0.4538	0.0699
Google-AdHoc-MAGLEV	0.2030	0.5644	0.2471	0.4289	0.0657
Google-AdHoc-BM25	0.2060	0.5674	0.2490	0.4107	0.0554
Google-AdHoc-Neural	0.1410	0.4068	0.1732	0.2823	0.0074
bioinfo-1	0.2180	0.5925	0.2630	0.4707	0.0713
bioinfo-2	0.2280	0.6006	0.2724	0.4810	0.0796
bioinfo-3	0.2180	0.5801	0.2615	0.4792	0.0479
bioinfo-4	0.2260	0.5893	0.2697	0.4677	0.0645
bio-answerfinder	0.3956	0.4295	0.3566	0.3693	0.0180
submit mesh index qa	0.1940	0.5462	0.2368	0.4021	0.0530
same as one	0.1980	0.5410	0.2409	0.4248	0.0525
Deep ML methods for	0.1960	0.5454	0.2376	0.4094	0.0430
MindLab QA System	0.1936	0.5022	0.2314	0.3740	0.0297
Ir_sys1	0.2000	0.5473	0.2443	0.3898	0.0342
Ir_sys2	0.1880	0.5406	0.2304	0.3910	0.0356
Ir_sys3	0.1840	0.5445	0.2280	0.4047	0.0350
Ir_sys4	0.1980	0.5544	0.2415	0.3959	0.0365
lalala	0.2040	0.5755	0.2480	0.4312	0.0507
bioinfo-0	0.2280	0.6008	0.2729	0.4809	0.0734
pa	0.3721	0.5325	0.3874	0.4115	0.0421
Google-AdHoc-M-Full	0.2030	0.5644	0.2471	0.4289	0.0657

Snippets

System	Mean precision	Recall	F-Measure	MAP	GMAP
AUEB-System1	0.2594	0.3498	0.2569	1.1139	0.0232
AUEB-System2	0.2453	0.3442	0.2469	1.0662	0.0201
AUEB-System3	0.1589	0.1279	0.1158	0.2272	0.0060
AUEB-System4	0.2217	0.2706	0.2036	0.4353	0.0195
AUEB-System5	0.1759	0.1725	0.1410	0.4027	0.0063
Google-MAGLEV-RERANK	-	-	-	-	-
Google-AdHoc-MAGLEV	-	-	-	-	-
Google-AdHoc-BM25	-	-	-	-	-
Google-AdHoc-Neural	-	-	-	-	-
bioinfo-1	0.1918	0.2682	0.1967	0.3732	0.0043
bioinfo-2	0.2032	0.2750	0.2036	0.4019	0.0056
bioinfo-3	0.2000	0.2832	0.2064	0.3839	0.0051
bioinfo-4	0.1928	0.2686	0.1968	0.3632	0.0041
bio-answerfinder	0.2304	0.2855	0.2172	0.5869	0.0093
submit mesh index qa	-	-	-	-	-
same as one	-	-	-	-	-
Deep ML methods for	0.1422	0.1959	0.1343	0.4432	0.0057
MindLab QA System	0.1366	0.1873	0.1290	0.4286	0.0042
Ir_sys1	-	-	-	-	-
Ir_sys2	-	-	-	-	-
Ir_sys3	-	-	-	-	-
Ir_sys4	-	-	-	-	-
lalala	-	-	-	-	-
bioinfo-0	-	-	-	-	-
pa	0.2359	0.3315	0.2322	0.8045	0.0198
Google-AdHoc-M-Full	-	-	-	-	-

Concepts

System	Mean precision	Recall	F-Measure	MAP	GMAP
AUEB-System1	-	-	-	-	-
AUEB-System2	-	-	-	-	-
AUEB-System3	-	-	-	-	-
AUEB-System4	-	-	-	-	-
AUEB-System5	-	-	-	-	-
Google-MAGLEV-RERANK	-	-	-	-	-
Google-AdHoc-MAGLEV	-	-	-	-	-
Google-AdHoc-BM25	-	-	-	-	-
Google-AdHoc-Neural	-	-	-	-	-
bioinfo-1	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
bioinfo-4	-	-	-	-	-
bio-answerfinder	-	-	-	-	-
submit mesh index qa	-	-	-	-	-
same as one	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
MindLab QA System	-	-	-	-	-
Ir_sys1	-	-	-	-	-
Ir_sys2	-	-	-	-	-
Ir_sys3	-	-	-	-	-
Ir_sys4	-	-	-	-	-
lalala	-	-	-	-	-
bioinfo-0	-	-	-	-	-
pa	-	-	-	-	-
Google-AdHoc-M-Full	-	-	-	-	-

RDF Triples

System	Mean precision	Recall	F-Measure	MAP	GMAP
AUEB-System1	-	-	-	-	-
AUEB-System2	-	-	-	-	-
AUEB-System3	-	-	-	-	-
AUEB-System4	-	-	-	-	-
AUEB-System5	-	-	-	-	-
Google-MAGLEV-RERANK	-	-	-	-	-
Google-AdHoc-MAGLEV	-	-	-	-	-
Google-AdHoc-BM25	-	-	-	-	-
Google-AdHoc-Neural	-	-	-	-	-
bioinfo-1	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
bioinfo-4	-	-	-	-	-
bio-answerfinder	-	-	-	-	-
submit mesh index qa	-	-	-	-	-
same as one	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
MindLab QA System	-	-	-	-	-
Ir_sys1	-	-	-	-	-
Ir_sys2	-	-	-	-	-
Ir_sys3	-	-	-	-	-
Ir_sys4	-	-	-	-	-
lalala	-	-	-	-	-
bioinfo-0	-	-	-	-	-
pa	-	-	-	-	-
Google-AdHoc-M-Full	-	-	-	-	-

Test batch 5

Documents

System	Mean precision	Recall	F-Measure	MAP	GMAP
Google-AdHoc-BM25	0.1540	0.5986	0.2222	0.4563	0.0346
Google-AdHoc-MAGLEV	0.1640	0.6330	0.2364	0.4743	0.0411
Google-AdHoc-Neural	0.1000	0.4512	0.1503	0.3298	0.0075
Google-MAGLEV-RERANK	0.1640	0.6330	0.2364	0.4795	0.0408
Google-AdHoc-M-Full	0.1540	0.5986	0.2222	0.4580	0.0354
AUEB-System1	0.1909	0.6097	0.2590	0.4954	0.0464
AUEB-System2	0.1889	0.6257	0.2587	0.4970	0.0510
AUEB-System3	0.1909	0.6097	0.2590	0.3745	0.0340
AUEB-System4	0.1909	0.6097	0.2590	0.4953	0.0462
AUEB-System5	0.1779	0.5879	0.2443	0.4408	0.0358
bio-answerfinder	0.3160	0.3928	0.3131	0.3294	0.0048
bioinfo-0	0.1690	0.6371	0.2420	0.5061	0.0545
bioinfo-1	0.1700	0.6407	0.2435	0.5098	0.0652
bioinfo-2	0.1660	0.6221	0.2372	0.4983	0.0477
bioinfo-3	0.1670	0.6340	0.2396	0.4981	0.0528
bioinfo-4	0.1680	0.6428	0.2410	0.5095	0.0555
Deep ML methods for	0.1400	0.5467	0.2007	0.4126	0.0287
Ir_sys1	0.1380	0.5849	0.2038	0.4147	0.0219
Ir_sys2	0.1440	0.5750	0.2084	0.4549	0.0297
Ir_sys3	0.1470	0.5879	0.2126	0.4516	0.0370
Ir_sys4	0.1520	0.6137	0.2210	0.4432	0.0392
lalala	0.1460	0.6009	0.2142	0.4456	0.0333
MindLab QA System	0.1400	0.5467	0.2007	0.4123	0.0287
MindLab QA System ++	0.1400	0.5467	0.2007	0.4123	0.0287
pa	0.4083	0.6366	0.4505	0.5202	0.0634

Snippets

System	Mean precision	Recall	F-Measure	MAP	GMAP
Google-AdHoc-BM25	-	-	-	-	-
Google-AdHoc-MAGLEV	-	-	-	-	-
Google-AdHoc-Neural	-	-	-	-	-
Google-MAGLEV-RERANK	-	-	-	-	-
Google-AdHoc-M-Full	-	-	-	-	-
AUEB-System1	0.2557	0.4225	0.2955	1.0626	0.0248
AUEB-System2	0.2445	0.4095	0.2838	1.0342	0.0263
AUEB-System3	0.1189	0.1569	0.1195	0.2781	0.0016
AUEB-System4	0.1548	0.2338	0.1636	0.3656	0.0072
AUEB-System5	0.1386	0.1924	0.1404	0.3790	0.0028
bio-answerfinder	0.1784	0.2359	0.1734	0.4634	0.0018
bioinfo-0	-	-	-	-	-
bioinfo-1	0.1778	0.3296	0.2136	0.4416	0.0055
bioinfo-2	0.1778	0.3297	0.2150	0.4355	0.0050
bioinfo-3	0.1753	0.3270	0.2114	0.4333	0.0043
bioinfo-4	0.1746	0.3227	0.2103	0.4408	0.0053
Deep ML methods for	0.1205	0.2209	0.1460	0.4376	0.0020
Ir_sys1	-	-	-	-	-
Ir_sys2	-	-	-	-	-
Ir_sys3	-	-	-	-	-
Ir_sys4	-	-	-	-	-
lalala	-	-	-	-	-
MindLab QA System	0.0618	0.1524	0.0810	0.1094	0.0005
MindLab QA System ++	0.1205	0.2209	0.1460	0.4376	0.0020
pa	0.2390	0.3672	0.2687	1.1060	0.0103

Concepts

System	Mean precision	Recall	F-Measure	MAP	GMAP
Google-AdHoc-BM25	-	-	-	-	-
Google-AdHoc-MAGLEV	-	-	-	-	-
Google-AdHoc-Neural	-	-	-	-	-
Google-MAGLEV-RERANK	-	-	-	-	-
Google-AdHoc-M-Full	-	-	-	-	-
AUEB-System1	-	-	-	-	-
AUEB-System2	-	-	-	-	-
AUEB-System3	-	-	-	-	-
AUEB-System4	-	-	-	-	-
AUEB-System5	-	-	-	-	-
bio-answerfinder	-	-	-	-	-
bioinfo-0	-	-	-	-	-
bioinfo-1	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
bioinfo-4	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
Ir_sys1	-	-	-	-	-
Ir_sys2	-	-	-	-	-
Ir_sys3	-	-	-	-	-
Ir_sys4	-	-	-	-	-
lalala	-	-	-	-	-
MindLab QA System	-	-	-	-	-
MindLab QA System ++	-	-	-	-	-
pa	-	-	-	-	-

RDF Triples

System	Mean precision	Recall	F-Measure	MAP	GMAP
Google-AdHoc-BM25	-	-	-	-	-
Google-AdHoc-MAGLEV	-	-	-	-	-
Google-AdHoc-Neural	-	-	-	-	-
Google-MAGLEV-RERANK	-	-	-	-	-
Google-AdHoc-M-Full	-	-	-	-	-
AUEB-System1	-	-	-	-	-
AUEB-System2	-	-	-	-	-
AUEB-System3	-	-	-	-	-
AUEB-System4	-	-	-	-	-
AUEB-System5	-	-	-	-	-
bio-answerfinder	-	-	-	-	-
bioinfo-0	-	-	-	-	-
bioinfo-1	-	-	-	-	-
bioinfo-2	-	-	-	-	-
bioinfo-3	-	-	-	-	-
bioinfo-4	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
Ir_sys1	-	-	-	-	-
Ir_sys2	-	-	-	-	-
Ir_sys3	-	-	-	-	-
Ir_sys4	-	-	-	-	-
lalala	-	-	-	-	-
MindLab QA System	-	-	-	-	-
MindLab QA System ++	-	-	-	-	-
pa	-	-	-	-	-