BioASQ Participants Area

Task 6b: 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
htersys	0.0030	0.0075	0.0043	0.0030	0.0000
aueb-nlp-1	0.2965	0.6260	0.3372	0.2265	0.0639
aueb-nlp-2	0.2945	0.6307	0.3364	0.2275	0.0777
aueb-nlp-3	0.3015	0.6387	0.3438	0.2327	0.0686
sdm/rerank	0.2500	0.5648	0.2886	0.1840	0.0277
aueb-nlp-4	0.2965	0.6220	0.3365	0.2288	0.0574
OAQA based system	0.1385	0.3352	0.1581	0.1064	0.0014
MindLab QA System	0.0031	0.0031	0.0030	0.0012	0.0000
MindLab QA System ++	0.0030	0.0031	0.0030	0.0016	0.0000
testtext	0.2390	0.4206	0.2618	0.1742	0.0099
ustb_prir1	0.2390	0.4206	0.2618	0.1742	0.0099
ustb_prir2	0.2310	0.4041	0.2519	0.1709	0.0075
ustb_prir3	0.2380	0.4193	0.2605	0.1738	0.0115
ustb_prir4	0.2380	0.4193	0.2605	0.1738	0.0115

Snippets

System	Mean precision	Recall	F-Measure	MAP	GMAP
htersys	-	-	-	-	-
aueb-nlp-1	0.2156	0.2496	0.1938	0.1675	0.0108
aueb-nlp-2	0.2168	0.2599	0.1980	0.1642	0.0123
aueb-nlp-3	0.2224	0.2593	0.2004	0.1684	0.0118
sdm/rerank	0.0940	0.0870	0.0722	0.0572	0.0004
aueb-nlp-4	0.2127	0.2501	0.1924	0.1659	0.0111
OAQA based system	0.1318	0.1837	0.1322	0.1089	0.0005
MindLab QA System	0.0014	0.0013	0.0013	0.0004	0.0000
MindLab QA System ++	0.0014	0.0013	0.0013	0.0004	0.0000
testtext	0.1664	0.1723	0.1409	0.1151	0.0022
ustb_prir1	0.1664	0.1723	0.1409	0.1151	0.0022
ustb_prir2	0.1572	0.1616	0.1319	0.1205	0.0019
ustb_prir3	0.1691	0.1680	0.1393	0.1193	0.0018
ustb_prir4	0.1634	0.1619	0.1364	0.1209	0.0015

Concepts

System	Mean precision	Recall	F-Measure	MAP	GMAP
htersys	-	-	-	-	-
aueb-nlp-1	-	-	-	-	-
aueb-nlp-2	-	-	-	-	-
aueb-nlp-3	-	-	-	-	-
sdm/rerank	0.5313	0.4515	0.4360	0.1315	0.0186
aueb-nlp-4	-	-	-	-	-
OAQA based system	0.0174	0.0428	0.0245	0.0110	0.0000
MindLab QA System	-	-	-	-	-
MindLab QA System ++	-	-	-	-	-
testtext	0.1674	0.5154	0.2250	0.1246	0.0087
ustb_prir1	0.2019	0.5896	0.2688	0.1623	0.0117
ustb_prir2	0.1609	0.5015	0.2160	0.1117	0.0094
ustb_prir3	0.0957	0.3159	0.1405	0.0522	0.0005
ustb_prir4	0.1870	0.5090	0.2466	0.1360	0.0147

RDF Triples

System	Mean precision	Recall	F-Measure	MAP	GMAP
htersys	-	-	-	-	-
aueb-nlp-1	-	-	-	-	-
aueb-nlp-2	-	-	-	-	-
aueb-nlp-3	-	-	-	-	-
sdm/rerank	-	-	-	-	-
aueb-nlp-4	-	-	-	-	-
OAQA based system	-	-	-	-	-
MindLab QA System	-	-	-	-	-
MindLab QA System ++	-	-	-	-	-
testtext	-	-	-	-	-
ustb_prir1	-	-	-	-	-
ustb_prir2	-	-	-	-	-
ustb_prir3	-	-	-	-	-
ustb_prir4	-	-	-	-	-

Test batch 2

Documents

System	Mean precision	Recall	F-Measure	MAP	GMAP
sys1	0.2717	0.5568	0.3017	0.1897	0.0339
sys2	0.2687	0.5480	0.2974	0.1882	0.0312
sys3	0.2820	0.5945	0.3117	0.1940	0.0408
aueb-nlp-1	0.3060	0.6294	0.3332	0.2319	0.0560
aueb-nlp-2	0.3210	0.6420	0.3475	0.2470	0.0701
aueb-nlp-3	0.3160	0.6365	0.3423	0.2416	0.0646
aueb-nlp-4	0.3220	0.6431	0.3479	0.2500	0.0660
aueb-nlp-5	0.5091	0.5530	0.4493	0.2070	0.0508
sys4	0.2490	0.5663	0.2818	0.1885	0.0364
sys5	0.2650	0.5938	0.2956	0.1960	0.0414
OAQA based system	0.1563	0.3281	0.1647	0.1103	0.0029
sdm/rerank	0.2670	0.5717	0.2964	0.1891	0.0374
MindLab QA System	0.2310	0.4996	0.2503	0.1665	0.0259
testtext	0.3201	0.6355	0.3464	0.2467	0.0634
Deep ML methods for	0.1660	0.2493	0.1642	0.1220	0.0019
ustb_prir1	0.3201	0.6355	0.3464	0.2467	0.0634
ustb_prir2	0.3221	0.6618	0.3519	0.2458	0.0795
MindLab QA System ++	0.2310	0.4996	0.2503	0.1665	0.0259
ustb_prir3	0.3121	0.6379	0.3396	0.2512	0.0639
ustb_prir4	0.3121	0.6379	0.3396	0.2512	0.0639
MindLab QA Reloaded	0.2611	0.4996	0.2696	0.1425	0.0186

Snippets

System	Mean precision	Recall	F-Measure	MAP	GMAP
sys1	-	-	-	-	-
sys2	-	-	-	-	-
sys3	-	-	-	-	-
aueb-nlp-1	0.2716	0.2055	0.1749	0.2226	0.0202
aueb-nlp-2	0.2768	0.2133	0.1826	0.2256	0.0236
aueb-nlp-3	0.2746	0.2041	0.1749	0.2272	0.0210
aueb-nlp-4	0.2873	0.2146	0.1850	0.2337	0.0231
aueb-nlp-5	0.3852	0.2976	0.2653	0.3187	0.0352
sys4	-	-	-	-	-
sys5	-	-	-	-	-
OAQA based system	0.2016	0.1423	0.1387	0.1704	0.0013
sdm/rerank	0.1973	0.1111	0.1040	0.1571	0.0024
MindLab QA System	0.2888	0.2298	0.1986	0.2695	0.0071
testtext	0.2191	0.3904	0.2060	0.1463	0.0083
Deep ML methods for	0.1684	0.1054	0.0934	0.1488	0.0008
ustb_prir1	0.2191	0.3904	0.2060	0.1463	0.0083
ustb_prir2	0.2213	0.6154	0.2581	0.1727	0.0217
MindLab QA System ++	0.2878	0.2307	0.1985	0.2736	0.0065
ustb_prir3	0.2123	0.3819	0.2041	0.1421	0.0082
ustb_prir4	0.2179	0.6188	0.2566	0.1731	0.0205
MindLab QA Reloaded	0.2888	0.2298	0.1986	0.2695	0.0071

Concepts

System	Mean precision	Recall	F-Measure	MAP	GMAP
sys1	-	-	-	-	-
sys2	-	-	-	-	-
sys3	-	-	-	-	-
aueb-nlp-1	-	-	-	-	-
aueb-nlp-2	-	-	-	-	-
aueb-nlp-3	-	-	-	-	-
aueb-nlp-4	-	-	-	-	-
aueb-nlp-5	-	-	-	-	-
sys4	-	-	-	-	-
sys5	-	-	-	-	-
OAQA based system	0.0108	0.0288	0.0155	0.0108	0.0000
sdm/rerank	0.5566	0.4357	0.4445	0.0852	0.0132
MindLab QA System	-	-	-	-	-
testtext	0.1816	0.5692	0.2471	0.1317	0.0170
Deep ML methods for	-	-	-	-	-
ustb_prir1	0.1816	0.5984	0.2493	0.1426	0.0221
ustb_prir2	0.1526	0.5365	0.2163	0.1065	0.0228
MindLab QA System ++	-	-	-	-	-
ustb_prir3	0.1368	0.4796	0.1861	0.0960	0.0098
ustb_prir4	0.1737	0.5587	0.2344	0.1366	0.0172
MindLab QA Reloaded	-	-	-	-	-

RDF Triples

System	Mean precision	Recall	F-Measure	MAP	GMAP
sys1	-	-	-	-	-
sys2	-	-	-	-	-
sys3	-	-	-	-	-
aueb-nlp-1	-	-	-	-	-
aueb-nlp-2	-	-	-	-	-
aueb-nlp-3	-	-	-	-	-
aueb-nlp-4	-	-	-	-	-
aueb-nlp-5	-	-	-	-	-
sys4	-	-	-	-	-
sys5	-	-	-	-	-
OAQA based system	-	-	-	-	-
sdm/rerank	-	-	-	-	-
MindLab QA System	-	-	-	-	-
testtext	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
ustb_prir1	-	-	-	-	-
ustb_prir2	-	-	-	-	-
MindLab QA System ++	-	-	-	-	-
ustb_prir3	-	-	-	-	-
ustb_prir4	-	-	-	-	-
MindLab QA Reloaded	-	-	-	-	-

Test batch 3

Documents

System	Mean precision	Recall	F-Measure	MAP	GMAP
aueb-nlp-1	0.3437	0.5940	0.3432	0.2406	0.0824
aueb-nlp-2	0.3557	0.5931	0.3511	0.2549	0.0796
aueb-nlp-3	0.3477	0.5975	0.3462	0.2477	0.0844
aueb-nlp-4	0.3577	0.6035	0.3545	0.2556	0.0811
aueb-nlp-5	0.5926	0.5169	0.5000	0.2189	0.0563
sys1	0.3160	0.5530	0.3207	0.2018	0.0555
sys3	0.3187	0.5612	0.3229	0.2068	0.0578
sys4	0.2660	0.5523	0.2742	0.2013	0.0558
sys5	0.2800	0.5628	0.2862	0.2080	0.0579
sdm/rerank	0.3280	0.5951	0.3353	0.2193	0.0920
MindLab QA System ++	0.2450	0.5076	0.2503	0.1818	0.0391
MindLab QA Reloaded	0.2750	0.5485	0.2794	0.2091	0.0573
testtext	0.3637	0.6217	0.3655	0.2597	0.0991
ustb_prir1	0.3250	0.6140	0.3314	0.2500	0.0923
ustb_prir2	0.3340	0.6340	0.3396	0.2622	0.1057
ustb_prir3	0.3657	0.6244	0.3680	0.2610	0.0996
ustb_prir4	0.3160	0.6203	0.3251	0.2506	0.0965
MindLab QA System	0.2750	0.5485	0.2794	0.2091	0.0573
Deep ML methods for	0.1960	0.3469	0.1915	0.1165	0.0123
MindLab Red Lions++	0.1840	0.3093	0.1772	0.1065	0.0080
sys2	0.2660	0.5523	0.2742	0.2011	0.0557
OAQA based system	0.1530	0.2964	0.1470	0.1147	0.0027
YODAQA based system	0.1981	0.1304	0.1425	0.0494	0.0002

Snippets

System	Mean precision	Recall	F-Measure	MAP	GMAP
aueb-nlp-1	0.2588	0.2612	0.2304	0.2278	0.0415
aueb-nlp-2	0.2532	0.2486	0.2211	0.2197	0.0319
aueb-nlp-3	0.2515	0.2525	0.2214	0.2221	0.0337
aueb-nlp-4	0.2600	0.2607	0.2298	0.2306	0.0359
aueb-nlp-5	0.3807	0.3655	0.3452	0.3320	0.0536
sys1	-	-	-	-	-
sys3	-	-	-	-	-
sys4	-	-	-	-	-
sys5	-	-	-	-	-
sdm/rerank	0.1865	0.1552	0.1510	0.1533	0.0063
MindLab QA System ++	0.1935	0.2414	0.1976	0.1787	0.0057
MindLab QA Reloaded	0.2374	0.2832	0.2377	0.2217	0.0134
testtext	0.2951	0.2600	0.2452	0.2021	0.0246
ustb_prir1	0.2894	0.2557	0.2413	0.1977	0.0227
ustb_prir2	0.2727	0.2331	0.2236	0.1953	0.0274
ustb_prir3	0.2951	0.2600	0.2452	0.2021	0.0246
ustb_prir4	0.2738	0.2346	0.2253	0.1967	0.0277
MindLab QA System	0.2491	0.3094	0.2504	0.2113	0.0218
Deep ML methods for	0.2320	0.2814	0.2297	0.1889	0.0197
MindLab Red Lions++	0.2191	0.2592	0.2164	0.1991	0.0127
sys2	-	-	-	-	-
OAQA based system	0.1892	0.2137	0.1807	0.1730	0.0025
YODAQA based system	-	-	-	-	-

Concepts

System	Mean precision	Recall	F-Measure	MAP	GMAP
aueb-nlp-1	-	-	-	-	-
aueb-nlp-2	-	-	-	-	-
aueb-nlp-3	-	-	-	-	-
aueb-nlp-4	-	-	-	-	-
aueb-nlp-5	-	-	-	-	-
sys1	-	-	-	-	-
sys3	-	-	-	-	-
sys4	-	-	-	-	-
sys5	-	-	-	-	-
sdm/rerank	0.5340	0.4936	0.4605	0.0904	0.0111
MindLab QA System ++	-	-	-	-	-
MindLab QA Reloaded	-	-	-	-	-
testtext	0.1689	0.4794	0.2081	0.0966	0.0036
ustb_prir1	0.1746	0.5991	0.2421	0.1157	0.0121
ustb_prir2	0.1576	0.5597	0.2276	0.0931	0.0094
ustb_prir3	0.1000	0.4588	0.1559	0.0655	0.0032
ustb_prir4	0.1661	0.6057	0.2405	0.1234	0.0126
MindLab QA System	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
MindLab Red Lions++	-	-	-	-	-
sys2	-	-	-	-	-
OAQA based system	0.0068	0.0240	0.0105	0.0042	0.0000
YODAQA based system	-	-	-	-	-

RDF Triples

System	Mean precision	Recall	F-Measure	MAP	GMAP
aueb-nlp-1	-	-	-	-	-
aueb-nlp-2	-	-	-	-	-
aueb-nlp-3	-	-	-	-	-
aueb-nlp-4	-	-	-	-	-
aueb-nlp-5	-	-	-	-	-
sys1	-	-	-	-	-
sys3	-	-	-	-	-
sys4	-	-	-	-	-
sys5	-	-	-	-	-
sdm/rerank	-	-	-	-	-
MindLab QA System ++	-	-	-	-	-
MindLab QA Reloaded	-	-	-	-	-
testtext	-	-	-	-	-
ustb_prir1	-	-	-	-	-
ustb_prir2	-	-	-	-	-
ustb_prir3	-	-	-	-	-
ustb_prir4	-	-	-	-	-
MindLab QA System	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
MindLab Red Lions++	-	-	-	-	-
sys2	-	-	-	-	-
OAQA based system	-	-	-	-	-
YODAQA based system	0.7500	1.0000	0.8333	0.6972	0.6281

Test batch 4

Documents

System	Mean precision	Recall	F-Measure	MAP	GMAP
aueb-nlp-1	0.2436	0.5968	0.2749	0.1772	0.0225
aueb-nlp-2	0.2456	0.5886	0.2760	0.1822	0.0207
aueb-nlp-3	0.2466	0.5930	0.2779	0.1804	0.0218
aueb-nlp-4	0.2446	0.5877	0.2751	0.1843	0.0208
aueb-nlp-5	0.4245	0.5180	0.4052	0.1615	0.0150
sys1	0.2110	0.5846	0.2489	0.1501	0.0181
sys3	0.2220	0.5810	0.2567	0.1529	0.0189
sys4	0.2110	0.5842	0.2466	0.1532	0.0188
MindLab Red Lions++	0.1880	0.4643	0.2136	0.1418	0.0066
MindLab QA System ++	0.1880	0.4643	0.2136	0.1418	0.0066
sdm/rerank	0.2049	0.5788	0.2427	0.1407	0.0200
YODAQA based system	0.1603	0.1915	0.1469	0.0523	0.0003
sys2	0.2210	0.5922	0.2597	0.1480	0.0193
auth-qa-1	0.1609	0.2187	0.1549	0.0560	0.0003
sys5	0.2220	0.5840	0.2598	0.1491	0.0180
testtext	0.2449	0.6327	0.2815	0.1650	0.0281
ustb_prir1	0.2330	0.6244	0.2701	0.1665	0.0261
ustb_prir2	0.2450	0.6378	0.2827	0.1649	0.0275
ustb_prir3	0.2459	0.6328	0.2821	0.1654	0.0281
ustb_prir4	0.2290	0.6015	0.2640	0.1638	0.0205

Snippets

System	Mean precision	Recall	F-Measure	MAP	GMAP
aueb-nlp-1	0.1798	0.2377	0.1656	0.1444	0.0035
aueb-nlp-2	0.1822	0.2566	0.1742	0.1555	0.0038
aueb-nlp-3	0.1836	0.2513	0.1736	0.1480	0.0035
aueb-nlp-4	0.1761	0.2367	0.1646	0.1479	0.0033
aueb-nlp-5	0.2403	0.3229	0.2368	0.2138	0.0061
sys1	-	-	-	-	-
sys3	-	-	-	-	-
sys4	-	-	-	-	-
MindLab Red Lions++	0.1316	0.1877	0.1343	0.1151	0.0013
MindLab QA System ++	0.1480	0.2342	0.1579	0.1413	0.0015
sdm/rerank	0.0910	0.0919	0.0772	0.0532	0.0004
YODAQA based system	-	-	-	-	-
sys2	-	-	-	-	-
auth-qa-1	-	-	-	-	-
sys5	-	-	-	-	-
testtext	0.1748	0.3027	0.1738	0.1213	0.0070
ustb_prir1	0.1752	0.3028	0.1753	0.1216	0.0077
ustb_prir2	0.1694	0.2969	0.1696	0.1204	0.0059
ustb_prir3	0.1749	0.3027	0.1739	0.1213	0.0070
ustb_prir4	0.1674	0.2953	0.1669	0.1213	0.0061

Concepts

System	Mean precision	Recall	F-Measure	MAP	GMAP
aueb-nlp-1	-	-	-	-	-
aueb-nlp-2	-	-	-	-	-
aueb-nlp-3	-	-	-	-	-
aueb-nlp-4	-	-	-	-	-
aueb-nlp-5	-	-	-	-	-
sys1	-	-	-	-	-
sys3	-	-	-	-	-
sys4	-	-	-	-	-
MindLab Red Lions++	-	-	-	-	-
MindLab QA System ++	-	-	-	-	-
sdm/rerank	0.5651	0.4807	0.4910	0.0951	0.0107
YODAQA based system	-	-	-	-	-
sys2	-	-	-	-	-
auth-qa-1	-	-	-	-	-
sys5	-	-	-	-	-
testtext	0.1973	0.6313	0.2684	0.1250	0.0112
ustb_prir1	0.2108	0.7599	0.2999	0.1419	0.0302
ustb_prir2	0.1500	0.6622	0.2292	0.0876	0.0202
ustb_prir3	0.1324	0.6608	0.2074	0.0884	0.0128
ustb_prir4	0.1973	0.7378	0.2861	0.1342	0.0305

RDF Triples

System	Mean precision	Recall	F-Measure	MAP	GMAP
aueb-nlp-1	-	-	-	-	-
aueb-nlp-2	-	-	-	-	-
aueb-nlp-3	-	-	-	-	-
aueb-nlp-4	-	-	-	-	-
aueb-nlp-5	-	-	-	-	-
sys1	-	-	-	-	-
sys3	-	-	-	-	-
sys4	-	-	-	-	-
MindLab Red Lions++	-	-	-	-	-
MindLab QA System ++	-	-	-	-	-
sdm/rerank	-	-	-	-	-
YODAQA based system	-	-	-	-	-
sys2	-	-	-	-	-
auth-qa-1	-	-	-	-	-
sys5	-	-	-	-	-
testtext	-	-	-	-	-
ustb_prir1	-	-	-	-	-
ustb_prir2	-	-	-	-	-
ustb_prir3	-	-	-	-	-
ustb_prir4	-	-	-	-	-

Test batch 5

Documents

System	Mean precision	Recall	F-Measure	MAP	GMAP
Olelo system at HPI	0.1028	0.1104	0.0858	0.0464	0.0002
aueb-nlp-1	0.2195	0.4197	0.2400	0.1403	0.0095
aueb-nlp-2	0.2215	0.4356	0.2435	0.1416	0.0128
aueb-nlp-3	0.2175	0.4200	0.2373	0.1422	0.0100
aueb-nlp-4	0.2265	0.4424	0.2487	0.1464	0.0122
aueb-nlp-5	0.2702	0.3591	0.2552	0.1250	0.0077
sdm/rerank	0.1975	0.4002	0.2230	0.1328	0.0074
MindLab QA Reloaded	0.1750	0.3345	0.1944	0.1100	0.0051
MindLab QA System	0.1750	0.3345	0.1944	0.1100	0.0051
sys1	0.1830	0.3701	0.2043	0.1176	0.0042
sys2	0.1825	0.3585	0.2034	0.1174	0.0040
sys3	0.1805	0.3547	0.2009	0.1170	0.0039
MindLab Red Lions++	0.1750	0.3345	0.1944	0.1100	0.0051
sys4	0.1820	0.3539	0.2036	0.1142	0.0039
sys5	0.1800	0.3520	0.2001	0.1169	0.0035
MindLab QA System ++	0.1750	0.3345	0.1944	0.1100	0.0051
Deep ML methods for	0.1750	0.3345	0.1944	0.1100	0.0051
testtext	0.2095	0.4070	0.2330	0.1248	0.0089
ustb_prir1	0.2090	0.3994	0.2312	0.1268	0.0072
ustb_prir2	0.2125	0.4073	0.2375	0.1278	0.0093
ustb_prir3	0.2065	0.4029	0.2303	0.1230	0.0083
ustb_prir4	0.2035	0.3913	0.2266	0.1224	0.0071

Snippets

System	Mean precision	Recall	F-Measure	MAP	GMAP
Olelo system at HPI	0.0788	0.0595	0.0556	0.0351	0.0001
aueb-nlp-1	0.1362	0.1755	0.1224	0.0927	0.0024
aueb-nlp-2	0.1364	0.1781	0.1238	0.0967	0.0028
aueb-nlp-3	0.1303	0.1706	0.1175	0.0929	0.0021
aueb-nlp-4	0.1409	0.1849	0.1285	0.1007	0.0024
aueb-nlp-5	0.1360	0.1669	0.1248	0.1147	0.0020
sdm/rerank	0.0701	0.0561	0.0529	0.0441	0.0002
MindLab QA Reloaded	0.0908	0.1411	0.0911	0.1000	0.0005
MindLab QA System	0.0923	0.1444	0.0926	0.1006	0.0005
sys1	-	-	-	-	-
sys2	-	-	-	-	-
sys3	-	-	-	-	-
MindLab Red Lions++	0.0876	0.1446	0.0910	0.0874	0.0005
sys4	-	-	-	-	-
sys5	-	-	-	-	-
MindLab QA System ++	0.0872	0.1515	0.0919	0.0797	0.0006
Deep ML methods for	0.0813	0.1479	0.0858	0.0730	0.0005
testtext	0.1256	0.1485	0.1112	0.0861	0.0008
ustb_prir1	0.1264	0.1424	0.1100	0.0865	0.0010
ustb_prir2	0.1190	0.1473	0.1091	0.0798	0.0009
ustb_prir3	0.1237	0.1495	0.1108	0.0845	0.0009
ustb_prir4	0.1185	0.1498	0.1063	0.0814	0.0008

Concepts

System	Mean precision	Recall	F-Measure	MAP	GMAP
Olelo system at HPI	-	-	-	-	-
aueb-nlp-1	-	-	-	-	-
aueb-nlp-2	-	-	-	-	-
aueb-nlp-3	-	-	-	-	-
aueb-nlp-4	-	-	-	-	-
aueb-nlp-5	-	-	-	-	-
sdm/rerank	0.5875	0.3895	0.4051	0.0935	0.0150
MindLab QA Reloaded	-	-	-	-	-
MindLab QA System	-	-	-	-	-
sys1	-	-	-	-	-
sys2	-	-	-	-	-
sys3	-	-	-	-	-
MindLab Red Lions++	-	-	-	-	-
sys4	-	-	-	-	-
sys5	-	-	-	-	-
MindLab QA System ++	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
testtext	0.1931	0.4965	0.2527	0.1467	0.0128
ustb_prir1	0.2172	0.5942	0.2908	0.1796	0.0318
ustb_prir2	0.1793	0.5483	0.2505	0.1311	0.0249
ustb_prir3	0.2034	0.5661	0.2709	0.1417	0.0201
ustb_prir4	0.2241	0.6591	0.3030	0.1734	0.0461

RDF Triples

System	Mean precision	Recall	F-Measure	MAP	GMAP
Olelo system at HPI	-	-	-	-	-
aueb-nlp-1	-	-	-	-	-
aueb-nlp-2	-	-	-	-	-
aueb-nlp-3	-	-	-	-	-
aueb-nlp-4	-	-	-	-	-
aueb-nlp-5	-	-	-	-	-
sdm/rerank	-	-	-	-	-
MindLab QA Reloaded	-	-	-	-	-
MindLab QA System	-	-	-	-	-
sys1	-	-	-	-	-
sys2	-	-	-	-	-
sys3	-	-	-	-	-
MindLab Red Lions++	-	-	-	-	-
sys4	-	-	-	-	-
sys5	-	-	-	-	-
MindLab QA System ++	-	-	-	-	-
Deep ML methods for	-	-	-	-	-
testtext	-	-	-	-	-
ustb_prir1	-	-	-	-	-
ustb_prir2	-	-	-	-	-
ustb_prir3	-	-	-	-	-
ustb_prir4	-	-	-	-	-

Copyright © 2023, the BioASQ Challenge (http://bioasq.org)

Platinum sponsors of the BioASQ Challenge between 2012 and 2023

Google was a proud sponsor of the BioASQ Challenge from 2018 to 2022.
BioASQ was supported by the National Library of Medicine of the National Institutes of Health under award number R13LM012214. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
The research leading to these results has received funding from the European Commission's Seventh Framework Programme (FP7/2007-2013, ICT-2011.4.4(d), Intelligent Information Management, Targeted Competition Framework) under grant agreement n° 318652.