Treebanks ranked by best Words
max maxteam avg stdev
1. fr_spoken 100.00 AntNLP 96.15 ± 3.77
2. fro_srcmf 100.00 AntNLP 96.15 ± 3.77
3. ko_kaist 100.00 AntNLP 96.15 ± 3.77
4. sl_sst 100.00 AntNLP 96.15 ± 3.77
5. grc_proiel 100.00 AntNLP 96.15 ± 3.77
6. ur_udtb 100.00 AntNLP 96.15 ± 3.77
7. hi_hdtb 100.00 AntNLP 96.15 ± 3.77
8. got_proiel 100.00 AntNLP 96.15 ± 3.77
9. id_gsd 100.00 AntNLP 96.15 ± 3.77
10. sk_snk 100.00 AntNLP 96.15 ± 3.77
11. la_proiel 100.00 UDPipe Future 96.15 ± 3.77
12. cs_fictree 100.00 UDPipe Future 96.12 ± 3.77
13. eu_bdt 100.00 Uppsala 96.12 ± 3.77
14. cu_proiel 100.00 AntNLP 92.31 ± 7.39
15. la_perseus 100.00 AntNLP 92.24 ± 7.39
16. fi_ftb 99.99 AntNLP 96.14 ± 3.77
17. cs_cac 99.99 UDPipe Future 96.12 ± 3.77
18. sv_lines 99.99 Uppsala 96.11 ± 3.77
19. la_ittb 99.99 IBM NY 96.10 ± 3.77
20. no_nynorsklia 99.99 AntNLP 92.23 ± 7.39
21. grc_perseus 99.98 Stanford 96.12 ± 3.77
22. ca_ancora 99.98 UDPipe Future 96.11 ± 3.77
23. pl_sz 99.98 Stanford 95.99 ± 3.77
24. sr_set 99.97 AntNLP 96.12 ± 3.77
25. es_ancora 99.97 Stanford 96.09 ± 3.77
26. af_afribooms 99.97 NLP-Cube 95.92 ± 3.76
27. en_lines 99.96 Uppsala 96.10 ± 3.77
28. cs_pdt 99.96 Stanford 96.09 ± 3.77
29. sv_talbanken 99.96 Uppsala 95.97 ± 3.76
30. no_nynorsk 99.96 NLP-Cube 92.24 ± 7.39
31. et_edt 99.96 HIT-SCIR 92.23 ± 7.38
32. nl_alpino 99.95 Stanford 95.98 ± 3.77
33. sl_ssj 99.95 Stanford 90.98 ± 7.30
34. pl_lfg 99.94 NLP-Cube 96.03 ± 3.77
35. bg_btb 99.93 NLP-Cube 96.08 ± 3.77
36. hr_set 99.93 UDPipe Future 96.08 ± 3.77
37. el_gdt 99.92 HIT-SCIR 95.98 ± 3.77
38. ug_udt 99.91 NLP-Cube 91.68 ± 7.34
39. da_ddt 99.90 UDPipe Future 96.02 ± 3.77
40. nl_lassysmall 99.88 Stanford 95.98 ± 3.76
41. ko_gsd 99.88 Stanford 95.97 ± 3.76
42. hu_szeged 99.87 Stanford 95.97 ± 3.76
43. no_bokmaal 99.87 NLP-Cube 92.11 ± 7.38
44. sme_giella 99.85 IBM NY 91.83 ± 7.51
45. uk_iu 99.83 Stanford 92.00 ± 7.37
46. en_gum 99.81 Stanford 95.91 ± 3.76
47. it_isdt 99.78 Stanford 99.68 ± 0.04
48. fi_tdt 99.78 Uppsala 92.02 ± 7.37
49. ro_rrt 99.77 Stanford 92.01 ± 7.37
50. lv_lvtb 99.75 Stanford 91.80 ± 7.35
51. en_pud 99.74 AntNLP 91.99 ± 7.37
52. ru_syntagrus 99.71 NLP-Cube 91.95 ± 7.36
53. pcm_nsc 99.71 CEA LIST 79.94 ±10.69
54. sv_pud 99.69 IBM NY 90.93 ± 7.28
55. fi_pud 99.69 Uppsala 88.13 ±10.81
56. fa_seraji 99.68 Stanford 95.79 ± 3.76
57. pt_bosque 99.63 Stanford 95.63 ± 3.77
58. de_gsd 99.62 NLP-Cube 95.72 ± 3.76
59. cs_pud 99.62 Uppsala 91.64 ± 7.34
60. ga_idt 99.60 Uppsala 91.31 ± 7.55
61. fr_sequoia 99.47 IBM NY 95.27 ± 3.75
62. fr_gsd 99.47 IBM NY 95.02 ± 3.74
63. it_postwita 99.47 UDPipe Future 91.76 ± 7.35
64. fo_oft 99.47 CUNI x-ling 86.76 ±10.68
65. gl_ctg 99.43 Stanford 91.47 ± 7.33
66. en_ewt 99.26 NLP-Cube 95.22 ± 3.74
67. bxr_bdt 99.24 IBM NY 88.64 ± 8.09
68. gl_treegal 98.73 AntNLP 94.44 ± 3.97
69. hsb_ufal 98.64 UDPipe Future 94.10 ± 4.51
70. ru_taiga 98.14 AntNLP 94.12 ± 3.77
71. tr_imst 97.92 AntNLP 94.15 ± 3.69
72. hy_armtdp 97.49 IBM NY 91.72 ± 6.05
73. kk_ktb 97.40 Uppsala 85.55 ± 7.45
74. kmr_mg 96.97 Uppsala 86.61 ± 7.16
75. ar_padt 96.81 Stanford 86.62 ± 7.00
76. zh_gsd 96.71 HIT-SCIR 86.91 ± 3.83
77. ja_gsd 94.53 HIT-SCIR 90.99 ± 1.13
78. he_htb 93.98 Stanford 82.45 ± 3.80
79. vi_vtb 93.46 HIT-SCIR 81.71 ± 3.73
80. br_keb 92.45 TurkuNLP 83.76 ± 7.37
81. ja_modern 75.69 HIT-SCIR 59.40 ± 7.70
82. th_pud 69.93 Uppsala 17.16 ±20.57