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Croatian (hr)

SETimes.HR treebank

Versions

Obtaining and License

The corpus is available on-line for free download under the CC BY-SA 3.0 license. The license in short:

SETimes.HR was created by Željko Agić (Universität Potsdam) and Nikola Ljubešić (Filozofski fakultet Sveučilišta u Zagrebu), Ivana Lučića 3, HR-10000 Zagreb, Croatia.

References

Domain

Croatian newspaper text from Southeast European Times.

Size

Version 1 contains 178,981 tokens in 7995 sentences, yielding 22.39 tokens per sentence on average. The file is a mixture of trees and non-trees, as only 2490 sentences have been annotated on the syntactic level. Part of the corpus (up to line number 93124) contains manually assigned lemmas and morphosyntactic descriptions (tags), while the rest contains automatic morphological annotation.

The improved pre-release version contains 83640 tokens in 3736 sentences, yielding 22.39 tokens per sentence on average.

Inside

All sentences in the improved pre-release version are manually annotated on morphological and syntactic levels. The officially available version 1 is a mixture of manual and automatic annotation, see the section on sizes above.

The treebank is distributed in the CoNLL 2006 file format. Multext-East morphosyntactic tags appear in both the CPOS and POS columns, while the FEAT column is empty.

In Version 1, if there is a token that has empty (“_”) value of the DEPREL column, then the sentence has not been syntactically annotated (even though there are numbers in the HEAD column; these are fake head links, typically they refer to the same node).

All sentences in the improved pre-release contain dependency information; however, at a few places there are errors introduced by the annotation software that result in a cyclic graph (not a tree).

The syntactic tags (DEPREL) are simplistic but somewhat inspired by the Prague Dependency Treebank, there are only 15 of them:

Tag Percent Example Description
Adv 5% Kosovu adverbial modifier
Ap 3% Esat appositional modifier, incl. first name attached to last name
Atr 26% privatizacije attribute modifying a noun phrase
Atv 2% iskoristiti ?
Aux 7% se ?
Co 3% a conjunction as coordination head (Prague-style coordinations)
Elp 0.6% Proces ellipsis
Obj 7% privatizacije object of a verb
Oth 2% Barem other
Pnom 2% složen nominal predicate attached to copula
Pred 10% analizira predicate (verbal)
Prep 10% na preposition
Punc 13% . punctuation
Sb 7% Kosovo subject
Sub 4% da subordinating conjunction

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Sample

The first three sentences of the CoNLL 2006 training data:

1 Глава _ N Nc _ 0 ROOT 0 ROOT
2 трета _ M Mo gen=f|num=s|def=i 1 mod 1 mod
1 НАРОДНО _ A An gen=n|num=s|def=i 2 mod 2 mod
2 СЪБРАНИЕ _ N Nc gen=n|num=s|def=i 0 ROOT 0 ROOT
1 Народното _ A An gen=n|num=s|def=d 2 mod 2 mod
2 събрание _ N Nc gen=n|num=s|def=i 3 subj 3 subj
3 осъществява _ V Vpi trans=t|mood=i|tense=r|pers=3|num=s 0 ROOT 0 ROOT
4 законодателната _ A Af gen=f|num=s|def=d 5 mod 5 mod
5 власт _ N Nc _ 3 obj 3 obj
6 и _ C Cp _ 3 conj 3 conj
7 упражнява _ V Vpi trans=t|mood=i|tense=r|pers=3|num=s 3 conjarg 3 conjarg
8 парламентарен _ A Am gen=m|num=s|def=i 9 mod 9 mod
9 контрол _ N Nc gen=m|num=s|def=i 7 obj 7 obj
10 . _ Punct Punct _ 3 punct 3 punct

The first three sentences of the CoNLL 2006 test data:

1 Единственото _ A An gen=n|num=s|def=d 2 mod 2 mod
2 решение _ N Nc gen=n|num=s|def=i 0 ROOT 0 ROOT
1 Ерик _ N Np gen=m|num=s|def=i 0 ROOT 0 ROOT
2 Франк _ N Np gen=m|num=s|def=i 1 mod 1 mod
3 Ръсел _ H Hm gen=m|num=s|def=i 2 mod 2 mod
1 Пълен _ A Am gen=m|num=s|def=i 2 mod 2 mod
2 мрак _ N Nc gen=m|num=s|def=i 0 ROOT 0 ROOT
3 и _ C Cp _ 2 conj 2 conj
4 пълна _ A Af gen=f|num=s|def=i 5 mod 5 mod
5 самота _ N Nc _ 2 conjarg 2 conjarg
6 . _ Punct Punct _ 2 punct 2 punct

Parsing

Nonprojectivities in BTB are rare. Only 747 of the 196,151 tokens in the CoNLL 2006 version are attached nonprojectively (0.38%).

The results of the CoNLL 2006 shared task are available online. They have been published in (Buchholz and Marsi, 2006). The evaluation procedure was non-standard because it excluded punctuation tokens. These are the best results for Bulgarian:

Parser (Authors) LAS UAS
MST (McDonald et al.) 87.57 92.04
Malt (Nivre et al.) 87.41 91.72
Nara (Yuchang Cheng) 86.34 91.30

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