\appendix
\clearpage
\onecolumn


\section{CorefUD 1.4 Details}
\label{sec:data-references}

\begin{table}[H]
  \resizebox{\columnwidth}{!}{
  \begin{tabular}{llll}
    Ancient Greek  & PROIEL      & \grcproiel{}      & \cite{Haug2008CreatingAP} \\
    Ancient Hebrew  & PTNK       & \hboptnk{}        & \cite{swanson-etal-2024-towards} \\
    Catalan    & AnCora          & \caancora{}       & \cite{ancora,ancora-co} \\
    Czech      & PCEDT           & \cspcedt{}        & \cite{PCEDT2016coreference} \\
    Czech      & PDT             & \cspdt{}          & \cite{udpdtc} \\
    Czech      & PDTSC           & \cspdtsc{}        & \cite{pdtsc} \\
    Dutch      & OpenBoek        & \nlopenboek{}     & \cite{Cranenburgh_vanNoord_2022} \\
    English    & FantasyCoref    & \enfantasycoref{} & \cite{han-etal-2021-fantasycoref} \\
    English    & GUM             & \engum{}          & \cite{GUMdocumentation} \\
    English    & LitBank         & \enlitbank{}      & \cite{Bamman20Litbank} \\
    English    & ParCorFull      & \enparcorfull{}   & \cite{ParCorFullScheme} \\
    French     & ANCOR           & \francor{}        & \cite{muzerelle-etal-2014-ancor} \\
    French     & Democrat        & \frdemocrat{}     & \cite{democrat} \\
    French     & LitBankFr       & \frlitbankfr      & \cite{MelanieBecquet2024BookNLPfr} \\
    German     & ParCorFull      & \deparcorfull{}   & \cite{ParCorFullScheme} \\
    German     & PotsdamCC       & \depotsdamcc{}    & \cite{potsdamCC-2020} \\
    Hindi      & HDTB            & \hihdtb{}         & \cite{mujadia-etal-2016-coreference} \\
    Hungarian  & KorKor          & \hukorkor{}       & \cite{korkor_coling} \\
    Hungarian  & SzegedKoref     & \huszegedkoref{}  & \cite{szegedkoref2018} \\
    Korean     & ECMT            & \koecmt{}         & \cite{nam-etal-2020-effective} \\
    Latin      & CorefLat        & \lacoreflat{}     & \cite{delfino2024building} \\
    Lithuanian & LCC             & \ltlcc{}          & \cite{LithuanianScheme} \\
    Norwegian  & Bokmål NARC     & \nobokmaalnarc{}  & \cite{maehlum2022narc} \\
    Norwegian  & Nynorsk NARC    & \nonynorsknarc{}  & \cite{maehlum2022narc} \\
    Old Church Slavonic & PROIEL & \cuproiel         & \cite{Haug2008CreatingAP} \\
    Polish     & PCC             & \plpcc{}          & \cite{PCC2013,bookOgrodniczuk} \\
    Russian    & RuCor           & \rurucor{}        & \cite{RuCorDialog} \\
    Spanish    & AnCora          & \esancora{}       & \cite{ancora,ancora-co} \\
    Turkish    & ITCC            & \tritcc           & \cite{pamay2018coref} \\
  \end{tabular}
  }
  \caption{Datasets in CorefUD 1.4.}
  \label{tab:corefud}
\end{table}

%\formn{nechceme pridať ešte tabuľku so štatistikami cez splity?}

%\begin{table}
%\begin{tabular}{llrrrrrr}\toprule
%      % Wrd = syntactic words = non-empty nodes (!= surface tokens)
%      & & \MC{3}{\bf Train} & \MC{3}{\bf Dev}\\
%      \cmidrule(r){3-5}\cmidrule(l){6-8}
%      \bf Language & \bf Dataset & \bf Sents & \bf Words & \bf EmptyN & \bf Sents & \bf Words & \bf EmptyN \\\midrule
%      Ancient Greek & PROIEL       &  5,735 &  56,131 &  5,474 &   329 &   3,600 &   365 \\
%      Ancient Hebrew & PTNK        &    312 &   7,727 &        &   439 &  10,531 &       \\
%      Catalan & AnCora             & 10,638 & 332,877 &  4,999 & 1,443 &  48,916 &   779 \\
%      Czech & PCEDT                & 39,832 & 935,568 & 28,886 & 6,960 & 164,112 & 5,072 \\
%      Czech & PDT                  & 38,725 & 653,720 & 16,709 & 5,228 &  88,156 & 2,440 \\
%      English & GUM                &  9,521 & 164,020 &     88 & 1,341 &  23,909 &    12 \\
%      English & LitBank            &  6,960 & 168,247 &        &   903 &  21,265 &       \\
%      English & ParCorFull         &    457 &   8,765 &        &    48 &   1,155 &       \\
%      French & Democrat            & 10,382 & 228,100 &        & 1,192 &  28,279 &       \\
%      German & ParCorFull          &    457 &   8,649 &        &    48 &   1,098 &       \\
%      German & PotsdamCC           &  1,817 &  26,677 &        &   216 &   3,376 &       \\
%      Hungarian & KorKor           &  1,086 &  19,457 &  1,606 &   130 &   2,524 &   191 \\
%      Hungarian & SzegedKoref      &  7,138 & 100,508 &  3,920 &   846 &  11,897 &   458 \\
%      Lithuanian & LCC             &  1,330 &  30,082 &        &   213 &   3,385 &       \\
%      Norwegian & BokmaalNARC      & 13,071 & 203,220 &        & 1,317 &  21,658 &       \\
%      Norwegian & NynorskNARC      & 10,320 & 172,764 &        & 1,158 &  17,977 &       \\
%      Old Church Slavonic & PROIEL &  5,211 &  47,853 &  4,914 &   918 &   7,813 &   772 \\
%      Polish & PCC                 & 28,722 & 431,618 & 14,802 & 3,573 &  53,948 & 1,872 \\
%      Russian & RuCor              &  7,069 & 123,599 &        & 1,286 &  21,139 &       \\
%      Spanish & AnCora             & 11,336 & 366,903 &  6,499 & 1,367 &  45,888 &   780 \\
%      Turkish & ITCC               &  3,531 &  45,125 &  9,418 &   556 &   4,868 &   953 \\
%      \bottomrule
%    \end{tabular}
%\caption{CorefUD 1.2 train and dev parts in terms of the total number of sentences, tokens and empty nodes.}
%\label{tab:sizes-train-dev}
%\end{table}

\section{CoNLL results by head UPOS}
\label{sec:stats-upos}

\begin{table}[H]\centering
\begin{tabular}{@{}l r@{~~~}r@{~~~}r@{~~~}r@{~~~}r@{~~~}r@{~~~}r@{~~~}r@{}}\toprule
\bf system         & \bf NOUN  & \bf PRON  & \bf PROPN & \bf DET   & \bf ADJ   & \bf VERB  & \bf ADV   & \bf NUM  \\\midrule
CorPipeEnsemble    & \bf 73.67 & \bf 78.12 & \bf 79.18 & \bf 56.51 & \bf 44.29 & \bf 38.34 &     30.19 & \bf 46.49 \\
CorPipeXXL         &     72.34 &     76.76 &     77.64 &     53.52 &     43.39 &     37.86 & \bf 32.48 &     43.54 \\
LLM-LatticeNLP     &     71.18 &     71.89 &     76.64 &     52.90 &     37.92 &     32.30 &     24.55 &     38.19 \\
LLM-UWB            &     70.80 &     70.40 &     75.77 &     50.46 &     38.01 &     32.51 &     25.67 &     37.21 \\
CorPipeLarge       &     68.29 &     73.17 &     73.35 &     49.15 &     37.63 &     30.57 &     27.54 &     36.95 \\
LLM-PortNLP        &     65.10 &     66.28 &     70.69 &     51.11 &     40.86 &     28.26 &     29.80 &     36.99 \\
DAggerCoref        &     64.28 &     67.68 &     67.11 &     43.41 &     30.55 &     28.37 &     23.32 &     30.23 \\
Stanza             &     62.81 &     67.42 &     67.96 &     43.56 &     32.35 &     25.02 &     23.05 &     31.66 \\
\baselinegz        &     48.35 &     55.61 &     53.77 &     34.58 &     22.45 &     15.02 &     19.80 &     21.17 \\
\baseline          &     47.73 &     54.36 &     53.30 &     33.33 &     22.29 &     14.11 &     20.47 &     21.32 \\
LLM-Landcore       &     41.68 &     41.36 &     50.70 &     24.12 &     19.24 &      6.41 &      8.38 &     11.55 \\
AUKBC-MULCRF       &     25.08 &     22.91 &     29.62 &      9.43 &      3.74 &      4.61 &      4.39 &      5.26 \\
\bottomrule\end{tabular}
\caption[CoNLL by UPOS entities]{CoNLL F$_1$ score (head-match) evaluated only on \textbf{entities} with heads of a given
UPOS. In both the gold and prediction files we deleted some entities before
running the evaluation. We kept only entities with at least one mention with
a given head UPOS (universal part of speech tag). For the purpose of this
analysis,
 if the head node had deprel=flat children,
 their UPOS tags were considered as well,
 so for example in ``Mr./NOUN Brown/PROPN''
 both NOUN and PROPN were taken as head UPOS,
 so the entity with this mention will be reported in both columns NOUN and PROPN.
Otherwise, the CoNLL F$_1$ scores are the same as in the primary metric,
 i.e.\ an unweighted average over all datasets, head-match, without singletons.
Note that when distinguishing entities into events and nominal entities,
 the VERB column can be considered as an approximation of the performance on events.
% One of the limitations of this approach is that copula is not treated as head in the Universal Dependencies,
%  so, e.g., phrase \textit{She is nice} is not considered for the VERB column,
%  but for the ADJ column (head of the phrase is \textit{nice}).

\hskip5mm The results for each UPOS mostly follow the overall system ordering with a few exceptions,
e.g., CorPipeLarge being the third system in PRON but fifth overall,
%e.g., CorPipeLarge being better than LLM-LatticeNLP and LLM-UWB in PRON,
or LLM-PortNLP being the third best system in ADJ but sixth best overall.
The easiest to predict are entities with PROPN or PRON,
while the most difficult are entities with ADV or VERB
(which are however quite rare both in the gold and predicted data, cf. Table~\ref{tab:stats-details}).
}
\label{tab:upos-entity}
\end{table}


\begin{table}[H]\centering
\begin{tabular}{@{}l r@{~~~}r@{~~~}r@{~~~}r@{~~~}r@{~~~}r@{~~~}r@{~~~}r@{}}\toprule
\bf system         & \bf NOUN  & \bf PRON  & \bf PROPN & \bf DET   & \bf ADJ   & \bf VERB  & \bf ADV   & \bf NUM  \\\midrule
CorPipeEnsemble    & \bf 64.73 & \bf 70.06 & \bf 64.38 & \bf 55.63 & \bf 51.74 & \bf 51.32 & \bf 52.28 & \bf 51.82 \\
CorPipeXXL         &     63.80 &     69.11 &     63.84 &     54.32 &     50.30 &     50.28 &     51.03 &     50.62 \\
LLM-LatticeNLP     &     61.93 &     63.71 &     62.34 &     53.98 &     49.74 &     49.07 &     49.92 &     49.66 \\
LLM-UWB            &     62.22 &     62.17 &     61.39 &     52.20 &     48.86 &     48.34 &     48.92 &     48.69 \\
CorPipeLarge       &     59.07 &     64.50 &     59.11 &     49.40 &     45.25 &     44.99 &     45.44 &     45.16 \\
LLM-PortNLP        &     55.57 &     57.93 &     57.01 &     48.35 &     44.41 &     43.84 &     44.85 &     44.33 \\
DAggerCoref        &     54.26 &     58.81 &     53.13 &     43.34 &     39.85 &     39.20 &     39.95 &     39.27 \\
Stanza             &     53.31 &     59.14 &     54.70 &     44.42 &     40.62 &     40.17 &     40.97 &     40.56 \\
\baselinegz        &     39.73 &     47.55 &     41.77 &     32.11 &     29.43 &     28.60 &     29.50 &     28.74 \\
\baseline          &     39.17 &     46.41 &     41.09 &     31.24 &     28.38 &     27.64 &     28.59 &     27.73 \\
LLM-Landcore       &     37.46 &     34.88 &     39.47 &     25.77 &     24.87 &     23.76 &     24.61 &     24.35 \\
AUKBC-MULCRF       &     29.90 &     27.87 &     29.28 &     13.29 &     13.49 &     13.64 &     13.94 &     13.92 \\
\bottomrule\end{tabular}
\caption[]{CoNLL F$_1$ score (head-match) evaluated only on \textbf{mentions} with heads of a given UPOS.
In both the gold and prediction files we deleted some mentions before running the evaluation.
We kept only mentions with a given head UPOS (again considering also deprel=flat children).
These results may be a bit
misleading because e.g.\ the PRON column does not consider all pronominal
coreference, but only pronoun-to-pronoun coreference. An entity with one
pronoun and one noun mention is excluded from this table (because it becomes
a singleton after deleting noun or pronoun mentions and singletons are
excluded from the evaluation in this table).

\hskip5mm The results again mostly follow the overall ranking.
%with CorPipeLarge being the third system in PRON, but fifth overall.
The easiest to predict are PRON-to-PRON coreferences.
}
\label{tab:upos-mention}
\end{table}

%\clearpage
\section{Statistics of the submitted systems on concatenation of all test sets}
\label{sec:stats-concat}
The systems are sorted alphabetically in the tables in this section.

\begin{table}[H]\centering
\begin{tabular}{@{}l rrrrr rrrrr@{}}\toprule
                    & \MC{5}{entities}                          & \MC{5}{distribution of lengths}     \\\cmidrule(lr){2-6}\cmidrule(l){7-11}
system              &   total & per 1k & \MC{2}{length} & range &    1 &     2 &     3 &     4 &   5+ \\\cmidrule(lr){4-5}
                    &   count &  words &    max &  avg. & p95   & [\%] &  [\%] &  [\%] &  [\%] & [\%] \\\midrule
gold                &  44,756 &    92 &   934 &  2.3 &   344 & 64.5 & 18.6 &  6.3 &  3.0 &  7.6 \\    
AUKBC-MULCRF        &  20,039 &    41 &   714 &  4.3 &   700 &  0.0 & 54.0 & 18.6 &  9.2 & 18.2 \\    
\baseline           &  13,469 &    28 &   411 &  4.8 &   787 &  0.0 & 54.7 & 17.1 &  8.2 & 20.0 \\    
\baselinegz         &  13,493 &    28 &   411 &  4.8 &   782 &  0.0 & 54.5 & 17.2 &  8.2 & 20.1 \\    
CorPipeEnsemble     &  46,670 &    96 &   920 &  2.3 &   332 & 64.4 & 18.4 &  6.5 &  3.3 &  7.4 \\    
CorPipeLarge        &  46,831 &    96 &   919 &  2.3 &   346 & 64.7 & 17.9 &  6.6 &  3.2 &  7.6 \\    
CorPipeXXL          &  46,272 &    95 &   929 &  2.3 &   341 & 64.5 & 18.3 &  6.4 &  3.3 &  7.5 \\    
DAggerCoref         &  14,896 &    31 &   776 &  4.8 &   414 &  2.1 & 48.8 & 18.1 &  9.0 & 22.0 \\    
LLM-Landcore        &  36,075 &    74 &   831 &  2.2 &   387 & 67.0 & 16.7 &  6.0 &  3.1 &  7.2 \\    
LLM-LatticeNLP      &  42,045 &    86 &   974 &  2.4 &   317 & 66.3 & 16.7 &  6.1 &  3.1 &  7.8 \\    
LLM-PortNLP         &  44,689 &    92 &   914 &  2.3 &   283 & 68.9 & 15.7 &  5.5 &  2.8 &  7.2 \\    
LLM-UWB             &  44,355 &    91 &   819 &  2.4 &   394 & 64.8 & 17.6 &  6.4 &  3.3 &  7.9 \\    
Stanza              &  38,825 &    80 &   924 &  2.5 &   313 & 59.4 & 21.7 &  7.2 &  3.4 &  8.3 \\    
\bottomrule\end{tabular}
\caption[]{Statistics on coreference entities.
The five column under the ``entities'' header are described in Table~\ref{tab:sizes}.
Here we add also distribution of entity lengths (singletons have length = 1).

\hskip5mm Note that the gold data contain 15,888 non-singleton entities,
 so the two baselines (that do not predict any singletons) undergenerate
 (i.e.\ predict less non-singleton entities than in the gold data),
 but AUKBC-MULCRF overgenerates (because 20,039$>$15,888).
These three systems and DAggerCoref also predict on average longer entities
 (i.e.\ with more mentions) than in the gold data.
The remaining systems have the statistics similar to the gold data,
 (although the CorPipe* systems slightly overgenerate,
 while LLM-Landcore and Stanza undergenerate).
}
\label{tab:stats-entities}
\end{table}

\begin{table}[H]\centering
\begin{tabular}{@{}l rrrr rrrrrr@{}}\toprule
                    & \MC{4}{non-singleton mentions}     & \MC{6}{distribution of lengths}              \\\cmidrule(lr){2-5}\cmidrule(l){6-11}
system              &   total & per 1k & \MC{2}{length} &     0 &     1 &     2 &     3 &     4 &   5+ \\\cmidrule(lr){4-5}
                    &   count &  words &    max &  avg. &  [\%] &  [\%] &  [\%] &  [\%] &  [\%] & [\%] \\\midrule
gold                &  76,034 &   156 &   100 &  2.2 &  9.5 & 52.5 & 19.4 &  6.5 &  2.9 &  9.2 \\    
AUKBC-MULCRF        &  85,220 &   175 &     6 &  1.0 &  7.1 & 89.6 &  2.8 &  0.4 &  0.1 &  0.0 \\    
\baseline           &  64,442 &   132 &    27 &  1.7 & 10.7 & 56.9 & 18.5 &  5.7 &  2.3 &  5.9 \\    
\baselinegz         &  64,583 &   132 &    27 &  1.7 & 10.9 & 56.8 & 18.4 &  5.7 &  2.3 &  6.0 \\    
CorPipeEnsemble     &  76,559 &   157 &   100 &  2.2 &  9.5 & 53.5 & 19.2 &  6.4 &  2.8 &  8.6 \\    
CorPipeLarge        &  77,022 &   158 &   119 &  2.2 &  9.4 & 53.6 & 19.3 &  6.3 &  2.7 &  8.6 \\    
CorPipeXXL          &  76,778 &   157 &   100 &  2.2 &  9.5 & 53.4 & 19.2 &  6.4 &  2.8 &  8.7 \\    
DAggerCoref         &  70,514 &   145 &     1 &  0.9 &  8.3 & 91.7 &  0.0 &  0.0 &  0.0 &  0.0 \\    
LLM-Landcore        &  55,494 &   114 &    62 &  1.9 &  6.6 & 56.5 & 20.2 &  6.8 &  2.8 &  7.0 \\    
LLM-LatticeNLP      &  72,236 &   148 &     1 &  0.9 &  9.6 & 90.4 &  0.0 &  0.0 &  0.0 &  0.0 \\    
LLM-PortNLP         &  70,571 &   145 &   100 &  2.1 &  9.6 & 55.0 & 19.1 &  5.9 &  2.6 &  7.9 \\    
LLM-UWB             &  75,958 &   156 &     1 &  0.9 &  9.5 & 90.5 &  0.0 &  0.0 &  0.0 &  0.0 \\    
Stanza              &  72,410 &   148 &   104 &  2.1 &  9.6 & 54.6 & 18.8 &  6.1 &  2.6 &  8.3 \\    
\bottomrule\end{tabular}
\caption[]{Statistics on non-singleton mentions.
The total number of mentions and the average number of
mentions per 1000 words of running text. The maximum and average mention length, i.e., the number of nonempty nodes (words) in the mention. Distribution of mention lengths (zeros have length = 0).

\hskip5mm Only the CorPipe* systems and LLM-UWB generate a similar number of non-singleton mentions as in the gold data.
AUKBC-MULCRF overgenerates mentions, all other systems undergenerate mentions.
The average length of mentions predicted by
 AUKBC-MULCRF, DAggerCoref, LLM-LatticeNLP, and LLM-UWB is notably lower than in the gold data
 because they predicted single-word mentions only (or mostly in case of AUKBC-MULCRF).
All other systems have the distribution of mention lengths similar to the gold data,
although no system predicts long mentions (4 and 5+ words) more frequently than in the gold data.
}
\label{tab:stats-mentions-nonsingleton}
\end{table}

\begin{table}[H]\centering
\begin{tabular}{@{}l rrrr rrrrrr@{}}\toprule
                    & \MC{4}{    singleton mentions}     & \MC{6}{distribution of lengths}              \\\cmidrule(lr){2-5}\cmidrule(l){6-11}
system              &   total & per 1k & \MC{2}{length} &     0 &     1 &     2 &     3 &     4 &   5+ \\\cmidrule(lr){4-5}
                    &   count &  words &    max &  avg. &  [\%] &  [\%] &  [\%] &  [\%] &  [\%] & [\%] \\\midrule
gold                &  28,848 &    59 &    81 &  3.0 &  1.0 & 37.7 & 25.4 & 12.5 &  6.4 & 17.0 \\    
AUKBC-MULCRF        &       0 &     0 &     0 &  0.0 &  0.0 &  0.0 &  0.0 &  0.0 &  0.0 &  0.0 \\    
\baseline           &       0 &     0 &     0 &  0.0 &  0.0 &  0.0 &  0.0 &  0.0 &  0.0 &  0.0 \\    
\baselinegz         &       0 &     0 &     0 &  0.0 &  0.0 &  0.0 &  0.0 &  0.0 &  0.0 &  0.0 \\    
CorPipeEnsemble     &  30,061 &    62 &   133 &  2.9 &  1.2 & 36.9 & 26.9 & 12.6 &  6.3 & 16.2 \\    
CorPipeLarge        &  30,295 &    62 &   311 &  3.2 &  1.1 & 36.5 & 26.3 & 12.6 &  6.1 & 17.3 \\    
CorPipeXXL          &  29,858 &    61 &   133 &  2.9 &  1.2 & 37.1 & 26.7 & 12.6 &  6.1 & 16.3 \\    
DAggerCoref         &     315 &     1 &     1 &  0.9 &  7.3 & 92.7 &  0.0 &  0.0 &  0.0 &  0.0 \\    
LLM-Landcore        &  24,168 &    50 &    60 &  3.6 &  0.3 & 28.7 & 25.8 & 14.2 &  7.9 & 23.1 \\    
LLM-LatticeNLP      &  27,882 &    57 &     1 &  1.0 &  0.8 & 99.2 &  0.0 &  0.0 &  0.0 &  0.0 \\    
LLM-PortNLP         &  30,775 &    63 &    58 &  3.0 &  0.8 & 35.3 & 27.6 & 12.8 &  6.7 & 16.8 \\    
LLM-UWB             &  28,736 &    59 &     1 &  1.0 &  0.9 & 99.1 &  0.0 &  0.0 &  0.0 &  0.0 \\    
Stanza              &  23,075 &    47 &    58 &  2.9 &  1.1 & 39.5 & 24.1 & 12.4 &  6.4 & 16.4 \\    
\bottomrule\end{tabular}
\caption[]{Statistics on singleton mentions.
See the caption of Table~\ref{tab:stats-mentions-nonsingleton} for details.

\hskip5mm The two baseline systems and AUKBC-MULCRF do not attempt to predict singletons at all.
DAggerCoref heavily undergenerates singletons.
}
\label{tab:stats-mentions-singleton}
\end{table}

\begin{table}[H]\centering
\resizebox{\textwidth}{!}{
\begin{tabular}{@{}l @{\!}r@{~}r@{~}r @{~}r@{~}r@{~}r@{~}r@{~}r@{~}r@{~}r@{~}r@{~}r@{~}r@{}}\toprule
                    & \MC{3}{mention type [\%]}    & \MC{9}{distribution of head UPOS [\%]}      \\\cmidrule(lr){2-4}\cmidrule(l){5-14}
system              & w/empty & w/gap & non-tree
                                             &  NOUN &  PRON & PROPN &   DET &   ADJ &  VERB &   ADV &   NUM & \_~ & other \\\midrule
gold                &  10.7 &  0.6 &  1.7 & 35.3 & 36.8 & 14.9 &  5.7 &  1.2 &  1.6 &  1.9 &  0.4 &  1.5 &  0.7 \\    
AUKBC-MULCRF        &   7.1 &  0.0 &  1.1 & 42.4 & 43.6 & 11.6 &  0.0 &  0.0 &  0.0 &  2.4 &  0.0 &  0.0 &  0.0 \\    
\baseline           &  11.3 &  0.0 &  1.8 & 31.3 & 40.8 & 15.1 &  6.4 &  1.0 &  0.7 &  1.9 &  0.3 &  1.9 &  0.6 \\    
\baselinegz         &  11.5 &  0.0 &  1.8 & 31.2 & 41.0 & 15.1 &  6.4 &  1.0 &  0.7 &  1.9 &  0.3 &  1.7 &  0.6 \\    
CorPipeEnsemble     &  10.5 &  0.0 &  1.9 & 35.2 & 37.1 & 15.0 &  5.8 &  1.1 &  1.3 &  1.8 &  0.4 &  1.6 &  0.6 \\    
CorPipeLarge        &  10.2 &  0.0 &  2.3 & 35.4 & 37.0 & 14.9 &  5.8 &  1.1 &  1.3 &  1.8 &  0.4 &  1.7 &  0.6 \\    
CorPipeXXL          &  10.5 &  0.0 &  2.0 & 35.3 & 37.0 & 14.9 &  5.8 &  1.1 &  1.4 &  1.8 &  0.5 &  1.6 &  0.6 \\    
DAggerCoref         &   8.3 &  0.0 &  0.0 & 34.0 & 39.8 & 15.0 &  6.1 &  1.1 &  1.2 &  1.9 &  0.4 &  0.0 &  0.7 \\    
LLM-Landcore        &   6.8 &  0.0 &  2.3 & 37.3 & 28.1 & 17.6 &  5.0 &  1.1 &  1.0 &  1.9 &  0.4 &  6.6 &  1.0 \\    
LLM-LatticeNLP      &   9.6 &  0.0 &  0.0 & 34.0 & 30.6 & 15.1 &  5.9 &  1.0 &  1.1 &  1.7 &  0.4 &  9.6 &  0.6 \\    
LLM-PortNLP         &  10.3 &  0.0 &  1.8 & 33.8 & 31.2 & 14.7 &  6.0 &  1.0 &  1.0 &  1.6 &  0.4 &  9.7 &  0.6 \\    
LLM-UWB             &   9.5 &  0.0 &  0.0 & 35.5 & 29.0 & 15.3 &  5.6 &  1.1 &  1.2 &  1.7 &  0.4 &  9.5 &  0.6 \\    
Stanza              &  10.4 &  0.0 &  1.8 & 34.9 & 37.1 & 15.4 &  5.9 &  1.0 &  1.1 &  1.8 &  0.4 &  1.7 &  0.6 \\    
\bottomrule\end{tabular}
}
\caption[]{Detailed statistics on non-singleton mentions.
The left part of the table shows the percentage of:
 mentions with at least one empty node (w/empty);
 mentions with at least one gap, i.e.\ discontinuous mentions (w/gap);
 and non-treelet mentions, i.e.\ mentions not forming a connected subgraph (catena) in the dependency tree (non-tree).
Note that these three types of mentions may be overlapping.

\hskip5mm  We can see that none of the systems attempts to predict discontinuous mentions.
AUKBC-MULCRF, DAggerCoref, LLM-LatticeNLP, and LLM-UWB 
 have a notably lower percentage of non-treelet mention spans
 (1.1\% for AUKBC-MULCRF, 0\% for the others),
 but this is simply explained by their inability to generate (enough) multi-word mentions.

\hskip5mm The right part of the table shows the distribution of mentions
  based on the universal part-of-speech tag (UPOS) of the head word.
Note that this distribution has to be interpreted with the total number of non-singleton mentions predicted (as reported in Table~\ref{tab:stats-mentions-nonsingleton}) in mind.
For example, 41.0\% of non-singleton mentions predicted by \baselinegz{} are pronominal (head=PRON),
  while there are only 35.3\% of pronominal non-singleton mentions in the gold data.
However, \baselinegz{} predicts actually less pronominal non-singleton mentions
 ($64{,}583 \times 41.0\% \approx 26{,}479$) than in the gold data ($76{,}034 \times 35.3\% \approx 26{,}840$).

\hskip5mm Note that the same word may be assigned a different UPOS tag in the predicted and gold data
(in case of empty nodes or if the gold data includes manual annotation).
The empty UPOS tag (\_) is present only in the empty nodes
and none of the LLM systems attempts to predict the actual UPOS tag of empty nodes.
Unlike last year, the baseline predictor of empty nodes predicts also their UPOS,
so the unconstrained systems using this predictor predict
that 79\% of the empty nodes are pronouns and 21\% have an empty UPOS
(which is very close to the 79\% of pronouns and 19\% of empty UPOS in the gold data).
}
\label{tab:stats-details}
\end{table}


\clearpage
\section{Statistics of the submitted systems on the French LitBankFr test set}
\label{sec:stats-litbankfr}
\input{stats-litbankfr}

\clearpage
\section{Statistics of the submitted systems on the Dutch OpenBoek test set}
\label{sec:stats-openboek}
\input{stats-openboek}

% \begin{table}[H]
% \resizebox{\textwidth}{!}{%
% \input{error_types_table}
% }
% \caption{Distribution of error types based on the methodology of \citet{kummerfeld-klein-2013-error}. By gradually transforming the prediction files into gold data, we can classify several types of transformations, which then map to types of errors. The number in the last row is the maximal total number of errors (summed over all datasets) of the given type, that any of the predictions made. The partially filled bars display the percentage of the maximal number of errors in the given column. The table should be viewed column-wise to compare individual prediction systems.
% The Span Errors column shows once again that Ondfa does not attempt to predict the whole span (only the head).
% CorPipe-single and CorPipe are the two worst systems in the number of Extra Entity and Extra Mention errors.
% However, according to Table~\ref{tab:secondary-metrics}, these systems have recall as high as precision,
% while other systems (e.g.\ Ondfa) have recall much lower;
% thus the high number of extra entities and mentions seems to be a good trade-off.
% Interestingly, CorPipe-2stage has the same recall as CorPipe (in almost all metric),
% but a slightly higher precision in Table~\ref{tab:secondary-metrics},
% which corresponds to the relatively lower number of Extra Entity and especially Extra Mention errors.
% }
% \label{tab:error-types}
% \end{table}

\clearpage
\section{Evolution of Codabench Submissions}
\label{sec:codalab-evol}
\begin{figure*}[h]
    \centering
    %\includegraphics[width=0.7\textwidth]{figures/mix_llm_unc/phase1_llm_vs_unc_nicks.pdf}
    \includegraphics[width=0.9\textwidth]{figures/mix_llm_unc/Figure_5-1.pdf}
    \caption{Evolution of Codabench Submissions in the %development (top) and test (bottom)
    evaluation phase. The submissions to the LLM and Unconstrained track are shown by using the dashed and solid lines, respectively.}
    \label{fig:codalab-evol}
\end{figure*}
