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Table of Contents

Treebanks for Various Languages

Arabic (ar)

Prague Arabic Dependency Treebank (PADT)

Versions

The CoNLL 2007 version reportedly improves over CoNLL 2006 in quality of morphological annotation. Both CoNLL versions miss important parts of the original PADT annotation (see below).

Obtaining and License

The original PADT 1.0 is distributed by the LDC under the catalogue number LDC2004T23. It is free for LDC members 2004, price for non-members is unknown (contact LDC). The license in short:

The CoNLL 2006 and 2007 versions are obtainable upon request under similar license terms. Their publication in the LDC together with the other CoNLL treebanks is being prepared.

PADT was created by members of the Institute of Formal and Applied Linguistics (Ústav formální a aplikované lingvistiky, ÚFAL), Faculty of Mathematics and Physics (Matematicko-fyzikální fakulta), Charles University in Prague (Univerzita Karlova v Praze), Malostranské náměstí 25, Praha, CZ-11800, Czechia. The CoNLL conversion of the treebank was prepared by Otakar Smrž, one of the key authors of the original PADT.

Domain

Newswire text from press agencies (Agence France Presse, Ummah, Al Hayat, An Nahar, Xinhua 2001-2003).

Size

According to their website, the original PADT 1.0 contains 113,500 tokens annotated analytically. The CoNLL 2006 version contains 59752 tokens in 1606 sentences, yielding 37.21 tokens per sentence on average (CoNLL 2006 data split: 54379 tokens / 1460 sentences training, 5373 tokens / 146 sentences test). The CoNLL 2007 version contains 116,793 tokens in 3043 sentences, yielding 38.38 tokens per sentence on average (CoNLL 2007 data split: 111,669 tokens / 2912 sentences training, 5124 tokens / 131 sentences test).

As noted in (Nivre et al., 2007), “the parsing units in this treebank are in many cases larger than conventional sentences, which partly explains the high average number of tokens per sentence.”

References

Inside

The original PADT 1.0 is distributed in the FS format. The CoNLL versions are distributed in the CoNLL-X format. The original PADT contains more information than the CoNLL version. There is morphological annotation (tags and lemmas) both manual and by a tagger (while only manual is in CoNLL data), glosses etc. However, the most important piece of information that got lost during the conversion to CoNLL is the FS attribute called parallel. It distinguishes conjuncts from shared modifiers of coordination and thus the syntactic structure is incomplete without it.

Word forms and lemmas are vocalized, i.e. they contain diacritics for short vowels as well as consonant gemination and a few other things. The CoNLL 2006 version includes Buckwalter transliteration of the Arabic script (in the same column as Arabic, attached to the Arabic form/lemma with an underscore character).

Note that tokenization of Arabic typically includes splitting original words (inserting spaces between letters), not just separating punctuation from words. Example: وبالفالوجة = wabiālfālūjah = wa/CONJ + bi/PREP + AlfAlwjp/NOUN_PROP = and in al-Falujah. In PADT, conjunctions and prepositions are separate tokens and nodes.

The original PADT 1.0 uses 10-character positional morphological tags whose documentation is hard to find. The CoNLL 2006 version converts the tags to the three CoNLL columns, CPOS, POS and FEAT, most of the information being encoded as pipe-separated attribute-value assignments in FEAT. There should be a 1-1 mapping between the PADT positional tags and the CoNLL 2006 annotation. The CoNLL 2007 version uses a tag conversion different from CoNLL 2006. Both CoNLL distributions contain a README file with a brief description of the parts of speech and features. Use DZ Interset to inspect the two CoNLL tagsets. Also look at the Elixir FM online interface for a later development of the morphological analyzer created along with PADT.

The guidelines for syntactic annotation are documented in the PADT annotation manual (only peculiarities of Arabic are documented, otherwise it is referenced to the annotation manual for the Czech treebank). The list and brief description of syntactic tags (dependency relation labels) can be found in (Smrž, Šnaidauf and Zemánek, 2002).

Sample

The first two sentences of the CoNLL 2006 training data:

1 غِيابُ_giyAbu غِياب_giyAb N N case=1|def=R 0 ExD _ _
2 فُؤاد_fu&Ad فُؤاد_fu&Ad Z Z _ 3 Atr _ _
3 كَنْعان_kanoEAn كَنْعان_kanoEAn Z Z _ 1 Atr _ _
1 فُؤاد_fu&Ad فُؤاد_fu&Ad Z Z _ 2 Atr _ _
2 كَنْعان_kanoEAn كَنْعان_kanoEAn Z Z _ 9 Sb _ _
3 ،_, ،_, G G _ 2 AuxG _ _
4 رائِد_rA}id رائِد_rA}id N N _ 2 Atr _ _
5 القِصَّة_AlqiS~ap قِصَّة_qiS~ap N N gen=F|num=S|def=D 4 Atr _ _
6 القَصِيرَةِ_AlqaSiyrapi قَصِير_qaSiyr A A gen=F|num=S|case=2|def=D 5 Atr _ _
7 فِي_fiy فِي_fiy P P _ 4 AuxP _ _
8 لُبْنانِ_lubonAni لُبْنان_lubonAn Z Z case=2|def=R 7 Atr _ _
9 رَحَلَ_raHala رَحَل-َ_raHal-a V VP pers=3|gen=M|num=S 0 Pred _ _
10 مَساءَ_masA'a مَساء_masA' D D _ 9 Adv _ _
11 أَمْسِ_>amosi أَمْسِ_>amosi D D _ 10 Atr _ _
12 عَن_Ean عَن_Ean P P _ 9 AuxP _ _
13 81_81 81_81 Q Q _ 12 Adv _ _
14 عاماً_EAmAF عام_EAm N N gen=M|num=S|case=4|def=I 13 Atr _ _
15 ._. ._. G G _ 0 AuxK _ _

The first sentence of the CoNLL 2006 test data:

1 اِتِّفاقٌ_Ait~ifAqN اِتِّفاق_Ait~ifAq N N case=1|def=I 0 ExD _ _
2 بَيْنَ_bayona بَيْنَ_bayona P P _ 1 AuxP _ _
3 لُبْنانِ_lubonAni لُبْنان_lubonAn Z Z case=2|def=R 4 Atr _ _
4 وَ_wa وَ_wa C C _ 2 Coord _ _
5 سُورِيَّةٍ_suwriy~apK سُورِيا_suwriyA Z Z gen=F|num=S|case=2|def=I 4 Atr _ _
6 عَلَى_EalaY عَلَى_EalaY P P _ 1 AuxP _ _
7 رَفْعِ_rafoEi رَفْع_rafoE N N case=2|def=R 6 Atr _ _
8 مُسْتَوَى_musotawaY مُسْتَوَى_musotawaY N N _ 7 Atr _ _
9 التَبادُلِ_AltabAduli تَبادُل_tabAdul N N case=2|def=D 8 Atr _ _
10 التِجارِيِّ_AltijAriy~i تِجارِيّ_tijAriy~ A A case=2|def=D 9 Atr _ _
11 إِلَى_<ilaY إِلَى_<ilaY P P _ 7 AuxP _ _
12 500_500 500_500 Q Q _ 11 Atr _ _
13 مِلْيُونِ_miloyuwni مِلْيُون_miloyuwn N N case=2|def=R 12 Atr _ _
14 دُولارٍ_duwlArK دُولار_duwlAr N N case=2|def=I 13 Atr _ _

The first sentence of the CoNLL 2007 training data:

1 تَعْدادُ تَعْداد_1 N N- Case=1|Defin=R 7 Sb _ _
2 سُكّانِ ساكِن_1 N N- Case=2|Defin=R 1 Atr _ _
3 22 [DEFAULT] Q Q- _ 2 Atr _ _
4 دَوْلَةً دَوْلَة_1 N N- Gender=F|Number=S|Case=4|Defin=I 3 Atr _ _
5 عَرَبِيَّةً عَرَبِيّ_1 A A- Gender=F|Number=S|Case=4|Defin=I 4 Atr _ _
6 سَ سَ_FUT F F- _ 7 AuxM _ _
7 يَرْتَفِعُ اِرْتَفَع_1 V VI Mood=I|Voice=A|Person=3|Gender=M|Number=S 0 Pred _ _
8 إِلَى إِلَى_1 P P- _ 7 AuxP _ _
9 654 [DEFAULT] Q Q- _ 8 Adv _ _
10 مِلْيُونَ مِلْيُون_1 N N- Case=4|Defin=R 9 Atr _ _
11 نَسَمَةٍ نَسَمَة_1 N N- Gender=F|Number=S|Case=2|Defin=I 10 Atr _ _
12 فِي فِي_1 P P- _ 7 AuxP _ _
13 مُنْتَصَفِ مُنْتَصَف_1 N N- Case=2|Defin=R 12 Adv _ _
14 القَرْنِ قَرْن_1 N N- Case=2|Defin=D 13 Atr _ _

The first sentence of the CoNLL 2007 test data:

1 مُقاوَمَةُ مُقاوَمَة_1 N N- Gender=F|Number=S|Case=1|Defin=R 0 ExD _ _
2 زَواجِ زَواج_1 N N- Case=2|Defin=R 1 Atr _ _
3 الطُلّابِ طالِب_1 N N- Case=2|Defin=D 2 Atr _ _
4 العُرْفِيِّ عُرْفِيّ_1 A A- Case=2|Defin=D 2 Atr _ _

Parsing

Nonprojectivities in PADT are rare. Only 431 of the 116,793 tokens in the CoNLL 2007 version are attached nonprojectively (0.37%).

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 Arabic:

Parser (Authors) LAS UAS
MST (McDonald et al.) 66.91 79.34
Basis (O'Neil) 66.71 78.54
Malt (Nivre et al.) 66.71 77.52
Edinburgh (Riedel et al.) 66.65 78.62

The results of the CoNLL 2007 shared task are available online. They have been published in (Nivre et al., 2007). The evaluation procedure was changed to include punctuation tokens. These are the best results for Arabic:

Parser (Authors) LAS UAS
Malt (Nilsson et al.) 76.52 85.81
Nakagawa 75.08 86.09
Malt (Hall et al.) 74.75 84.21
Sagae 74.71 84.04
Chen 74.65 83.49
Titov et al. 74.12 83.18

The two Malt parser results of 2007 (single malt and blended) are described in (Hall et al., 2007) and the details about the parser configuration are described here.

Bulgarian (bg)

BulTreeBank (BTB)

Versions

The original BTB is based on HPSG (head-driven phrase-structure grammar). The CoNLL version contains only the dependency information encoded in HPSG BulTreeBank.

Obtaining and License

Only the CoNLL version seems to be distributed but you may ask the creators about the HPSG version. For the dependency version, print the license, sign, scan, send to Kiril Simov (kivs (at) bultreebank (dot) org) and wait for the data. The license in short:

BTB was created by members of the Linguistic Modelling Department (Секция Лингвистично моделиране), Bulgarian Academy of Sciences (Българска академия на науките), Ул. Акад. Г. Бончев, Бл. 25 А, 1113 София, Bulgaria.

Domain

Unknown (“A set of Bulgarian sentences marked-up with detailed syntactic information. These sentences are mainly extracted from authentic Bulgarian texts. They are chosen with regards two criteria. First, they cover the variety of syntactic structures of Bulgarian. Second, they show the statistical distribution of these phenomena in real texts.”) At least part of it is probably news (Novinar, Sega, Standart).

Size

The CoNLL 2006 version contains 196,151 tokens in 13221 sentences, yielding 14.84 tokens per sentence on average (CoNLL 2006 data split: 190,217 tokens / 12823 sentences training, 5934 tokens / 398 sentences test).

References

Inside

The original morphosyntactic tags have been converted to fit into the three columns (CPOS, POS and FEAT) columns of the CoNLL format. There should be a 1-1 mapping between the BTB positional tags and the CoNLL 2006 annotation. Use DZ Interset to inspect the two CoNLL tagsets.

The morphological analysis does not include lemmas. The morphosyntactic tags have been assigned (probably) manually.

The guidelines for syntactic annotation are documented in the other technical report. The CoNLL distribution contains the BulTreeBankReadMe.html file with a brief description of the syntactic tags (dependency relation labels).

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

Bengali (bn)

Hyderabad Dependency Treebank (HyDT-Bangla)

Versions

There has been no official release of the treebank yet. There have been two as-is sample releases for the purposes of the NLP tools contests in parsing Indian languages, attached to the ICON 2009 and 2010 conferences.

Obtaining and License

There is no standard distribution channel for the treebank after the ICON 2010 evaluation period. Inquire at the LTRC (ltrc (at) iiit (dot) ac (dot) in) about the possibility of getting the data. The ICON 2010 license in short:

HyDT-Bangla is being created by members of the Language Technologies Research Centre, International Institute of Information Technology, Gachibowli, Hyderabad, 500032, India.

Domain

Unknown.

Size

HyDT-Bangla shows dependencies between chunks, not words. The node/tree ratio is thus much lower than in other treebanks. The ICON 2009 version came with a data split into three parts: training, development and test:

Part Sentences Chunks Ratio
Training 980 6449 6.58
Development 150 811 5.41
Test 150 961 6.41
TOTAL 1280 8221 6.42

The ICON 2010 version came with a data split into three parts: training, development and test:

Part Sentences Chunks Ratio Words Ratio
Training 979 6440 6.58 10305 10.52
Development 150 812 5.41 1196 7.97
Test 150 961 6.41 1350 9.00
TOTAL 1279 8213 6.42 12851 10.04

I have counted the sentences and chunks. The number of words comes from (Husain et al., 2010). Note that the paper gives the number of training sentences as 980 (instead of 979), which is a mistake. The last training sentence has the id 980 but there is no sentence with id 418.

Apparently the training-development-test data split was more or less identical in both years, except for the minor discrepancies (number of training sentences and development chunks).

References

Inside

The text uses the WX encoding of Indian letters. If we know what the original script is (Bengali in this case) we can map the WX encoding to the original characters in UTF-8. WX uses English letters so if there was embedded English (or other string using Latin letters) it will probably get lost during the conversion.

The CoNLL format contains only the chunk heads. The native SSF format shows the other words in the chunk, too, but it does not capture intra-chunk dependency relations. This is an example of a multi-word chunk: 

3       ((      NP      <fs af='rumAla,n,,sg,,d,0,0' head="rumAla" drel=k2:VGF name=NP3>
3.1     ekatA   QC      <fs af='eka,num,,,,,,'>
3.2     ledisa  JJ      <fs af='ledisa,unk,,,,,,'>
3.3     rumAla  NN      <fs af='rumAla,n,,sg,,d,0,0' name="rumAla">
        ))

In the CoNLL format, the CPOS column contains the chunk label (e.g. NP = noun phrase) and the POS column contains the part of speech of the chunk head.

Occasionally there are NULL nodes that do not correspond to any surface chunk or token. They represent ellided participants.

The syntactic tags (dependency relation labels) are karaka relations, i.e. deep syntactic roles according to the Pāṇinian grammar. There are separate versions of the treebank with fine-grained and coarse-grained syntactic tags.

According to (Husain et al., 2010), in the ICON 2010 version, the chunk tags, POS tags and inter-chunk dependencies (topology + tags) were annotated manually. The rest (lemma, morphosyntactic features, headword of chunk) was marked automatically.

Note: There have been cycles in the Hindi part of HyDT but no such problem occurs in the Bengali part.

Sample

The first sentence of the ICON 2010 training data (with fine-grained syntactic tags) in the Shakti format: 

<document id="">			
<head>
<annotated-resource name="HyDT-Bangla" version="0.5" type="dep-interchunk-only" layers="morph,pos,chunk,dep-interchunk-only" language="ben" date-of-release="20100831">
    <annotation-standard>
        <morph-standard name="Anncorra-morph" version="1.31" date="20080920" />
        <pos-standard name="Anncorra-pos" version="" date="20061215" />
        <chunk-standard name="Anncorra-chunk" version="" date="20061215" />
        <dependency-standard name="Anncorra-dep" version="2.0" date="" dep-tagset-granularity="6" />
    </annotation-standard>
</annotated-resource>		
</head>			
<Sentence id="1">
1	((	NP	<fs af='Age,adv,,,,,,' head="Agei" drel=k7t:VGF name=NP>
1.1	mudZira	NN	<fs af='mudZi,n,,sg,,o,era,era'>
1.2	Agei	NST	<fs af='Age,adv,,,,,,' name="Agei">
	))		
2	((	NP	<fs af='cA,n,,sg,,d,0,0' head="cA" drel=k1:VGF name=NP2>
2.1	praWama	QO	<fs af='praWama,num,,,,,,'>
2.2	kApa	NN	<fs af='kApa,unk,,,,,,'>
2.3	cA	NN	<fs af='cA,n,,sg,,d,0,0' name="cA">
	))		
3	((	VGF	<fs af='As,v,,,5,,A_yA+Ce,A' head="ese" name=VGF>
3.1	ese	VM	<fs af='As,v,,,7,,A,A' name="ese">
3.2	.	SYM	<fs af='.,punc,,,,,,'>
	))		
</Sentence>

And in the CoNLL format:

1 Agei Age NP NST lex-Age|cat-adv|gend-|num-|pers-|case-|vib-|tam-|head-Agei|name-NP 3 k7t _ _
2 cA cA NP NN lex-cA|cat-n|gend-|num-sg|pers-|case-d|vib-0|tam-0|head-cA|name-NP2 3 k1 _ _
3 ese As VGF VM lex-As|cat-v|gend-|num-|pers-5|case-|vib-A_yA+Ce|tam-A|head-ese|name-VGF 0 main _ _

And after conversion of the WX encoding to the Bengali script in UTF-8:

1 আগেই আগে NP NST lex-Age|cat-adv|gend-|num-|pers-|case-|vib-|tam-|head-Agei|name-NP 3 k7t _ _
2 চা চা NP NN lex-cA|cat-n|gend-|num-sg|pers-|case-d|vib-0|tam-0|head-cA|name-NP2 3 k1 _ _
3 এসে আস্ VGF VM lex-As|cat-v|gend-|num-|pers-5|case-|vib-A_yA+Ce|tam-A|head-ese|name-VGF 0 main _ _

The first sentence of the ICON 2010 development data (with fine-grained syntactic tags) in the Shakti format: 

<document id="">
<head>
<annotated-resource name="HyDT-Bangla" version="0.5" type="dep-interchunk-only" layers="morph,pos,chunk,dep-interchunk-only" language="ben" date-of-release="20100831">
    <annotation-standard>
        <morph-standard name="Anncorra-morph" version="1.31" date="20080920" />
        <pos-standard name="Anncorra-pos" version="" date="20061215" />
        <chunk-standard name="Anncorra-chunk" version="" date="20061215" />
        <dependency-standard name="Anncorra-dep" version="2.0" date="" dep-tagset-granularity="6" />
    </annotation-standard>
</annotated-resource>
</head>
<Sentence id="1">
1	((	NP	<fs af='parabarwIkAle,adv,,,,,,' head="parabarwIkAle" drel=k7t:VGF name=NP>
1.1	parabarwIkAle	NN	<fs af='parabarwIkAle,adv,,,,,,' name="parabarwIkAle">
	))		
2	((	NP	<fs af='aPisa-biyArAraxera,unk,,,,,,' head="aPisa-biyArAraxera" drel=r6:NP3 name=NP2>
2.1	aPisa-biyArAraxera	NN	<fs af='aPisa-biyArAraxera,unk,,,,,,' name="aPisa-biyArAraxera">
	))		
3	((	NP	<fs af='nAma,n,,sg,,d,0,0' head="nAma" drel=k2:VGNN name=NP3>
3.1	nAma	NN	<fs af='nAma,n,,sg,,d,0,0' name="nAma">
	))		
4	((	NP	<fs af='GoRaNA,unk,,,,,,' head="GoRaNA" drel=pof:VGNN name=NP4>
4.1	GoRaNA	NN	<fs af='GoRaNA,unk,,,,,,' name="GoRaNA">
	))		
5	((	VGNN	<fs af='kar,n,,,any,,,' head="karAra" drel=r6:NP5 name=VGNN>
5.1	karAra	VM	<fs af='kar,n,,,any,,,' name="karAra">
	))		
6	((	NP	<fs af='samay,unk,,,,,,' head="samay" drel=k7t:VGF name=NP5>
6.1	samay	NN	<fs af='samay,unk,,,,,,' name="samay">
	))		
7	((	NP	<fs af='animeRake,unk,,,,,,' head="animeRake" drel=k2:VGF name=NP6>
7.1	animeRake	NNP	<fs af='animeRake,unk,,,,,,' name="animeRake">
	))		
8	((	VGF	<fs af='sariye,unk,,,5,,0_rAKA+ka_ha+la,' head="sariye" name=VGF>
8.1	sariye	VM	<fs af='sariye,unk,,,,,,' name="sariye">
8.2	.	SYM	<fs af='.,punc,,,,,,'>
	))		
</Sentence>

And in the CoNLL format:

1 parabarwIkAle parabarwIkAle NP NN lex-parabarwIkAle|cat-adv|gend-|num-|pers-|case-|vib-|tam-|head-parabarwIkAle|name-NP 8 k7t _ _
2 aPisa-biyArAraxera aPisa-biyArAraxera NP NN lex-aPisa-biyArAraxera|cat-unk|gend-|num-|pers-|case-|vib-|tam-|head-aPisa-biyArAraxera|name-NP2 3 r6 _ _
3 nAma nAma NP NN lex-nAma|cat-n|gend-|num-sg|pers-|case-d|vib-0|tam-0|head-nAma|name-NP3 5 k2 _ _
4 GoRaNA GoRaNA NP NN lex-GoRaNA|cat-unk|gend-|num-|pers-|case-|vib-|tam-|head-GoRaNA|name-NP4 5 pof _ _
5 karAra kar VGNN VM lex-kar|cat-n|gend-|num-|pers-any|case-|vib-|tam-|head-karAra|name-VGNN 6 r6 _ _
6 samay samay NP NN lex-samay|cat-unk|gend-|num-|pers-|case-|vib-|tam-|head-samay|name-NP5 8 k7t _ _
7 animeRake animeRake NP NNP lex-animeRake|cat-unk|gend-|num-|pers-|case-|vib-|tam-|head-animeRake|name-NP6 8 k2 _ _
8 sariye sariye VGF VM lex-sariye|cat-unk|gend-|num-|pers-5|case-|vib-0_rAKA+ka_ha+la|tam-|head-sariye|name-VGF 0 main _ _

And after conversion of the WX encoding to the Bengali script in UTF-8:

1 পরবর্তীকালে পরবর্তীকালে NP NN lex-parabarwIkAle|cat-adv|gend-|num-|pers-|case-|vib-|tam-|head-parabarwIkAle|name-NP 8 k7t _ _
2 অফিস-বিযারারদের অফিস-বিযারারদের NP NN lex-aPisa-biyArAraxera|cat-unk|gend-|num-|pers-|case-|vib-|tam-|head-aPisa-biyArAraxera|name-NP2 3 r6 _ _
3 নাম নাম NP NN lex-nAma|cat-n|gend-|num-sg|pers-|case-d|vib-0|tam-0|head-nAma|name-NP3 5 k2 _ _
4 ঘোষণা ঘোষণা NP NN lex-GoRaNA|cat-unk|gend-|num-|pers-|case-|vib-|tam-|head-GoRaNA|name-NP4 5 pof _ _
5 করার কর্ VGNN VM lex-kar|cat-n|gend-|num-|pers-any|case-|vib-|tam-|head-karAra|name-VGNN 6 r6 _ _
6 সময্ সময্ NP NN lex-samay|cat-unk|gend-|num-|pers-|case-|vib-|tam-|head-samay|name-NP5 8 k7t _ _
7 অনিমেষকে অনিমেষকে NP NNP lex-animeRake|cat-unk|gend-|num-|pers-|case-|vib-|tam-|head-animeRake|name-NP6 8 k2 _ _
8 সরিযে সরিযে VGF VM lex-sariye|cat-unk|gend-|num-|pers-5|case-|vib-0_rAKA+ka_ha+la|tam-|head-sariye|name-VGF 0 main _ _

The first sentence of the ICON 2010 test data (with fine-grained syntactic tags) in the Shakti format: 

<document id="">
<head>
<annotated-resource name="HyDT-Bangla" version="0.5" type="dep-interchunk-only" layers="morph,pos,chunk,dep-interchunk-only" language="ben" date-of-release="20101013">
    <annotation-standard>
        <morph-standard name="Anncorra-morph" version="1.31" date="20080920" />
	<pos-standard name="Anncorra-pos" version="" date="20061215" />
	<chunk-standard name="Anncorra-chunk" version="" date="20061215" />
	<dependency-standard name="Anncorra-dep" version="2.0" date="" dep-tagset-granularity="6" />
    </annotation-standard>
<annotated-resource>
</head>
<Sentence id="1">
1	((	NP	<fs af='mAXabIlawA,n,,sg,,d,0,0' head="mAXabIlawA" drel=k1:VGF name=NP>
1.1	mAXabIlawA	NNP	<fs af='mAXabIlawA,n,,sg,,d,0,0' name="mAXabIlawA">
	))		
2	((	NP	<fs af='waKana,pn,,,,d,0,0' head="waKana" drel=k7t:VGF name=NP2>
2.1	waKana	PRP	<fs af='waKana,pn,,,,d,0,0' name="waKana">
	))		
3	((	NP	<fs af='hAwa,n,,sg,,o,era,era' head="hAwera" drel=r6:NP4 name=NP3>
3.1	hAwera	NN	<fs af='hAwa,n,,sg,,o,era,era' name="hAwera">
	))		
4	((	NP	<fs af='GadZi,unk,,,,,,' head="GadZi" drel=k2:VGNF name=NP4>
4.1	GadZi	NN	<fs af='GadZi,unk,,,,,,' name="GadZi">
	))		
5	((	VGNF	<fs af='Kul,v,,,5,,ne,ne' head="Kule" drel=vmod:VGF name=VGNF>
5.1	Kule	VM	<fs af='Kul,v,,,5,,ne,ne' name="Kule">
	))		
6	((	NP	<fs af='tebila,n,,sg,,d,me,me' head="tebile" drel=k7p:VGF name=NP5>
6.1	tebile	NN	<fs af='tebila,n,,sg,,d,me,me' name="tebile">
	))		
7	((	VGF	<fs af='rAK,v,,,5,,Cila,Cila' head="rAKaCila" name=VGF>
7.1	rAKaCila	VM	<fs af='rAK,v,,,5,,Cila,Cila' name="rAKaCila">
7.2	।	SYM	
	))		
</Sentence>

And in the CoNLL format:

1 mAXabIlawA mAXabIlawA NP NNP lex-mAXabIlawA|cat-n|gend-|num-sg|pers-|case-d|vib-0|tam-0|head-mAXabIlawA|name-NP 7 k1 _ _
2 waKana waKana NP PRP lex-waKana|cat-pn|gend-|num-|pers-|case-d|vib-0|tam-0|head-waKana|name-NP2 7 k7t _ _
3 hAwera hAwa NP NN lex-hAwa|cat-n|gend-|num-sg|pers-|case-o|vib-era|tam-era|head-hAwera|name-NP3 4 r6 _ _
4 GadZi GadZi NP NN lex-GadZi|cat-unk|gend-|num-|pers-|case-|vib-|tam-|head-GadZi|name-NP4 5 k2 _ _
5 Kule Kul VGNF VM lex-Kul|cat-v|gend-|num-|pers-5|case-|vib-ne|tam-ne|head-Kule|name-VGNF 7 vmod _ _
6 tebile tebila NP NN lex-tebila|cat-n|gend-|num-sg|pers-|case-d|vib-me|tam-me|head-tebile|name-NP5 7 k7p _ _
7 rAKaCila rAK VGF VM lex-rAK|cat-v|gend-|num-|pers-5|case-|vib-Cila|tam-Cila|head-rAKaCila|name-VGF 0 main _ _

And after conversion of the WX encoding to the Bengali script in UTF-8:

1 মাধবীলতা মাধবীলতা NP NNP lex-mAXabIlawA|cat-n|gend-|num-sg|pers-|case-d|vib-0|tam-0|head-mAXabIlawA|name-NP 7 k1 _ _
2 তখন তখন NP PRP lex-waKana|cat-pn|gend-|num-|pers-|case-d|vib-0|tam-0|head-waKana|name-NP2 7 k7t _ _
3 হাতের হাত NP NN lex-hAwa|cat-n|gend-|num-sg|pers-|case-o|vib-era|tam-era|head-hAwera|name-NP3 4 r6 _ _
4 ঘড়ি ঘড়ি NP NN lex-GadZi|cat-unk|gend-|num-|pers-|case-|vib-|tam-|head-GadZi|name-NP4 5 k2 _ _
5 খুলে খুল্ VGNF VM lex-Kul|cat-v|gend-|num-|pers-5|case-|vib-ne|tam-ne|head-Kule|name-VGNF 7 vmod _ _
6 টেবিলে টেবিল NP NN lex-tebila|cat-n|gend-|num-sg|pers-|case-d|vib-me|tam-me|head-tebile|name-NP5 7 k7p _ _
7 রাখছিল রাখ্ VGF VM lex-rAK|cat-v|gend-|num-|pers-5|case-|vib-Cila|tam-Cila|head-rAKaCila|name-VGF 0 main _ _

Parsing

Nonprojectivities in HyDT-Bangla are not frequent. Only 78 of the 7252 chunks in the training+development ICON 2010 version are attached nonprojectively (1.08%).

The results of the ICON 2009 NLP tools contest have been published in (Husain, 2009). There were two evaluation rounds, the first with the coarse-grained syntactic tags, the second with the fine-grained syntactic tags. To reward language independence, only systems that parsed all three languages were officially ranked. The following table presents the Bengali/coarse-grained results of the four officially ranked systems, and the best Bengali-only* system.

Parser (Authors) LAS UAS
Kolkata (De et al.)* 84.29 90.32
Hyderabad (Ambati et al.) 78.25 90.22
Malt (Nivre) 76.07 88.97
Malt+MST (Zeman) 71.49 86.89
Mannem 70.34 83.56

The results of the ICON 2010 NLP tools contest have been published in (Husain et al., 2010), page 6. These are the best results for Bengali with fine-grained syntactic tags:

Parser (Authors) LAS UAS
Attardi et al. 70.66 87.41
Kosaraju et al. 70.55 86.16
Kolachina et al. 70.14 87.10

Catalan (ca)

There is one treebank versions of which were known in different times under different names:

Versions

The dependency treebank Cat3LB was extracted automatically from an earlier constituent-based annotation (see Montserrat Civit, Ma. Antònia Martí, Núria Bufí: Cat3LB and Cast3LB: From Constituents to Dependencies. In: T. Salakoski et al. (eds.): FinTAL 2006, LNAI 4139, pp. 141–152, 2006, Springer, Berlin / Heidelberg)

Obtaining and License

The AnCora-CA corpus ought to be freely downloadable from its website. The download will not work for unregistered and not signed in users. The website offers creating new account but it is not automatic, one has to wait for approval.

Republication of the two CoNLL versions in LDC is planned but it has not happenned yet.

The CoNLL 2007 license in short:

AnCora-CA was created by members of the Centre de Llenguatge i Computació (CLiC), Universitat de Barcelona, Gran Via de les Corts Catalanes 585, E-08007 Barcelona, Spain.

Domain

Mostly newswire (EFE news, ACN Catalan news, Catalan version of El Periódico, 2000).

Size

The CoNLL 2007 version contains 435,860 tokens in 15125 sentences, yielding 28.82 tokens per sentence on average (CoNLL 2007 data split: 430,844 tokens / 14958 sentences training, 5016 tokens / 167 sentences test).

The CoNLL 2009 version contains 496,672 tokens in 16786 sentences, yielding 29.59 tokens per sentence on average (CoNLL 2009 data split: 390,302 tokens / 13200 sentences training, 53015 tokens / 1724 sentences development, 53355 tokens / 1862 sentences test).

References

Inside

The original morphosyntactic tags (EAGLES?) have been converted to fit into the three columns (CPOS, POS and FEAT) columns of the CoNLL 2006/7 format, resp. the two columns (POS and FEAT) of the CoNLL 2009 format. Note that the missing CPOS column is not the only difference between the two conversion schemes. Feature names and values in the FEAT column are different, too.

The morphosyntactic tags have been disambiguated manually. The CoNLL 2009 version also contains automatically disambiguated tags.

Multi-word expressions have been collapsed into one token, using underscore as the joining character. This includes named entities (e.g. La_Garrotxa, Ajuntament_de_Manresa, dilluns_4_de_juny) and prepositional compounds (pel_que_fa_al, d'_acord_amb, la_seva, a_més_de). Empty (underscore) tokens have been inserted to represent missing subjects (Catalan is a pro-drop language).

Sample

The first sentence of the CoNLL 2007 training data:

1 L' el d da num=s|gen=c 2 ESPEC _ _
2 Ajuntament_de_Manresa Ajuntament_de_Manresa n np _ 4 SUJ _ _
3 ha haver v va num=s|per=3|mod=i|ten=p 4 AUX _ _
4 posat_en_funcionament posar_en_funcionament v vm num=s|mod=p|gen=m 0 S _ _
5 tot tot d di num=s|gen=m 7 ESPEC _ _
6 un_seguit_de un_seguit_de d di num=p|gen=c 5 DET _ _
7 mesures mesura n nc num=p|gen=f 4 CD _ _
8 , , F Fc _ 10 PUNC _ _
9 la el d da num=s|gen=f 10 ESPEC _ _
10 majoria majoria n nc num=s|gen=f 7 _ _ _
11 informatives informatiu a aq num=p|gen=f 10 _ _ _
12 , , F Fc _ 10 PUNC _ _
13 que que p pr num=n|gen=c 14 SUJ _ _
14 tenen tenir v vm num=p|per=3|mod=i|ten=p 7 SF _ _
15 com_a com_a s sp for=s 14 CPRED _ _
16 finalitat finalitat n nc num=s|gen=f 15 SN _ _
17 minimitzar minimitzar v vm mod=n 14 CD _ _
18 els el d da num=p|gen=m 19 ESPEC _ _
19 efectes efecte n nc num=p|gen=m 17 SN _ _
20 de de s sp for=s 19 SP _ _
21 la el d da num=s|gen=f 22 ESPEC _ _
22 vaga vaga n nc num=s|gen=f 20 SN _ _
23 . . F Fp _ 4 PUNC _ _

The first sentence of the CoNLL 2007 test data:

1 Tot_i_que tot_i_que c cs _ 5 SUBORD _ _
2 ahir ahir r rg _ 5 CC _ _
3 hi hi p pp num=n|per=3|gen=c 5 MORF _ _
4 va anar v va num=s|per=3|mod=i|ten=p 5 AUX _ _
5 haver haver v va mod=n 15 AO _ _
6 una un d di num=s|gen=f 7 ESPEC _ _
7 reunió reunió n nc num=s|gen=f 5 CD _ _
8 de de s sp for=s 7 SP _ _
9 darrera darrer a ao num=s|gen=f 10 SADJ _ _
10 hora hora n nc num=s|gen=f 8 SN _ _
11 , , F Fc _ 5 PUNC _ _
12 no no r rn _ 15 MOD _ _
13 es es p p0 _ 15 PASS _ _
14 va anar v va num=s|per=3|mod=i|ten=p 15 AUX _ _
15 aconseguir aconseguir v vm mod=n 0 S _ _
16 acostar acostar v vm mod=n 15 SUJ _ _
17 posicions posició n nc num=p|gen=f 16 SN _ _
18 , , F Fc _ 23 PUNC _ _
19 de_manera_que de_manera_que c cs _ 23 SUBORD _ _
20 els el d da num=p|gen=m 21 ESPEC _ _
21 treballadors treballador n nc num=p|gen=m 23 SUJ _ _
22 han haver v va num=p|per=3|mod=i|ten=p 23 AUX _ _
23 decidit decidir v vm num=s|mod=p|gen=m 15 AO _ _
24 anar anar v vm mod=n 23 CD _ _
25 a a s sp for=s 24 CREG _ _
26 la el d da num=s|gen=f 27 ESPEC _ _
27 vaga vaga n nc num=s|gen=f 25 SN _ _
28 . . F Fp _ 15 PUNC _ _

The first sentence of the CoNLL 2009 training data:

1 El el el d d postype=article|gen=m|num=s postype=article|gen=m|num=s 2 2 spec spec _ _ _ _ _ _
2 Tribunal_Suprem Tribunal_Suprem Tribunal_Suprem n n postype=proper|gen=c|num=c postype=proper|gen=c|num=c 7 7 suj suj _ _ arg0-agt _ _ _
3 ( ( ( f f punct=bracket|punctenclose=open punct=bracket|punctenclose=open 4 4 f f _ _ _ _ _ _
4 TS TS TS n n postype=proper|gen=c|num=c postype=proper|gen=c|num=c 2 2 sn sn _ _ _ _ _ _
5 ) ) ) f f punct=bracket|punctenclose=close punct=bracket|punctenclose=close 4 4 f f _ _ _ _ _ _
6 ha haver haver v v postype=auxiliary|gen=c|num=s|person=3|mood=indicative|tense=present postype=auxiliary|gen=c|num=s|person=3|mood=indicative|tense=present 7 7 v v _ _ _ _ _ _
7 confirmat confirmar confirmar v v postype=main|gen=m|num=s|mood=pastparticiple postype=main|gen=m|num=s|mood=pastparticiple 0 0 sentence sentence Y confirmar.a32 _ _ _ _
8 la el el d d postype=article|gen=f|num=s postype=article|gen=f|num=s 9 9 spec spec _ _ _ _ _ _
9 condemna condemna condemna n n postype=common|gen=f|num=s postype=common|gen=f|num=s 7 7 cd cd _ _ arg1-pat _ _ _
10 a a a s s postype=preposition|gen=c|num=c postype=preposition|gen=c|num=c 9 9 sp sp _ _ _ _ _ _
11 quatre quatre quatre d d postype=numeral|gen=c|num=p postype=numeral|gen=c|num=p 12 12 spec spec _ _ _ _ _ _
12 anys any any n n postype=common|gen=m|num=p postype=common|gen=m|num=p 10 10 sn sn _ _ _ _ _ _
13 d' de de s s postype=preposition|gen=c|num=c postype=preposition|gen=c|num=c 12 12 sp sp _ _ _ _ _ _
14 inhabilitació inhabilitació inhabilitació n n postype=common|gen=f|num=s postype=common|gen=f|num=s 13 13 sn sn _ _ _ _ _ _
15 especial especial especial a a postype=qualificative|gen=c|num=s postype=qualificative|gen=c|num=s 14 14 s.a s.a _ _ _ _ _ _
16 i i i c c postype=coordinating postype=coordinating 12 9 coord coord _ _ _ _ _ _
17 una un un d d postype=indefinite|gen=f|num=s postype=numeral|gen=f|num=s 18 18 spec spec _ _ _ _ _ _
18 multa multa multa n n postype=common|gen=f|num=s postype=common|gen=f|num=s 12 9 sn sn _ _ _ _ _ _
19 de de de s s postype=preposition|gen=c|num=c postype=preposition|gen=c|num=c 18 18 sp sp _ _ _ _ _ _
20 3,6 3.6 3,6 z n _ postype=proper|gen=c|num=c 21 21 spec spec _ _ _ _ _ _
21 milions milió milió n n postype=common|gen=m|num=p postype=common|gen=m|num=p 19 19 sn sn _ _ _ _ _ _
22 de de de s s postype=preposition|gen=c|num=c postype=preposition|gen=c|num=c 21 21 sp sp _ _ _ _ _ _
23 pessetes pesseta pesseta z n postype=currency postype=common|gen=f|num=p 22 22 sn sn _ _ _ _ _ _
24 per per per s s postype=preposition|gen=c|num=c postype=preposition|gen=c|num=c 9 9 sp sp _ _ _ _ _ _
25 a a a s s postype=preposition|gen=c|num=c postype=preposition|gen=c|num=c 24 24 sp sp _ _ _ _ _ _
26 quatre quatre quatre d d postype=numeral|gen=c|num=p postype=numeral|gen=c|num=p 27 27 spec spec _ _ _ _ _ _
27 veterinaris veterinari veterinari n n postype=common|gen=m|num=p postype=common|gen=m|num=p 25 25 sn sn _ _ _ _ _ _
28 gironins gironí gironí a a postype=qualificative|gen=m|num=p postype=qualificative|gen=m|num=p 27 27 s.a s.a _ _ _ _ _ _
29 , , , f f punct=comma punct=comma 30 30 f f _ _ _ _ _ _
30 per per per s s postype=preposition|gen=c|num=c postype=preposition|gen=c|num=c 9 7 sp cc _ _ _ _ _ _
31 haver haver haver v n postype=auxiliary|gen=c|num=c|mood=infinitive postype=common|gen=m|num=s 33 33 v v _ _ _ _ _ _
32 -se ell ell p p gen=c|num=c|person=3 gen=c|num=c|person=3 33 33 morfema.pronominal morfema.pronominal _ _ _ _ _ _
33 beneficiat beneficiar beneficiat v a postype=main|gen=m|num=s|mood=pastparticiple postype=qualificative|gen=m|num=s|posfunction=participle 42 30 S S Y beneficiar.a2 _ _ _ _
34 dels del dels s s postype=preposition|gen=m|num=p|contracted=yes postype=preposition|gen=m|num=p|contracted=yes 33 33 creg creg _ _ _ arg1-null _ _
35 càrrecs càrrec càrrec n n postype=common|gen=m|num=p postype=common|gen=m|num=p 34 34 sn sn _ _ _ _ _ _
36 públics públic públic a a postype=qualificative|gen=m|num=p postype=qualificative|gen=m|num=p 35 35 s.a s.a _ _ _ _ _ _
37 que que que p p postype=relative|gen=c|num=c postype=relative|gen=c|num=c 39 39 cd cd _ _ _ _ arg1-pat _
38 _ _ _ p p _ _ 39 39 suj suj _ _ _ _ arg0-agt _
39 desenvolupaven desenvolupar desenvolupar v v postype=main|gen=c|num=p|person=3|mood=indicative|tense=imperfect postype=main|gen=c|num=p|person=3|mood=indicative|tense=imperfect 35 35 S S Y desenvolupar.a2 _ _ _ _
40 i i i c c postype=coordinating postype=coordinating 42 33 coord coord _ _ _ _ _ _
41 la_seva el_seu el_seu d d postype=possessive|gen=f|num=s|person=3 postype=possessive|gen=f|num=s|person=3 42 42 spec spec _ _ _ _ _ _
42 relació relació relació n n postype=common|gen=f|num=s postype=common|gen=f|num=s 30 33 sn cd _ _ _ _ _ _
43 amb amb amb s s postype=preposition|gen=c|num=c postype=preposition|gen=c|num=c 42 42 sp sp _ _ _ _ _ _
44 les el el d d postype=article|gen=f|num=p postype=article|gen=f|num=p 45 45 spec spec _ _ _ _ _ _
45 empreses empresa empresa n n postype=common|gen=f|num=p postype=common|gen=f|num=p 43 43 sn sn _ _ _ _ _ _
46 càrniques càrnic càrnic a a postype=qualificative|gen=f|num=p postype=qualificative|gen=f|num=p 45 45 s.a s.a _ _ _ _ _ _
47 de de de s s postype=preposition|gen=c|num=c postype=preposition|gen=c|num=c 45 45 sp sp _ _ _ _ _ _
48 la el el d d postype=article|gen=f|num=s postype=article|gen=f|num=s 49 49 spec spec _ _ _ _ _ _
49 zona zona zona n n postype=common|gen=f|num=s postype=common|gen=f|num=s 47 47 sn sn _ _ _ _ _ _
50 en en en s s postype=preposition|gen=c|num=c postype=preposition|gen=c|num=c 42 42 sp sp _ _ _ _ _ _
51 oferir oferir oferir v v postype=main|gen=c|num=c|mood=infinitive postype=main|gen=c|num=c|mood=infinitive 50 50 S S Y oferir.a32 _ _ _ _
52 -los ell ell p p postype=personal|gen=c|num=p|person=3 postype=personal|gen=c|num=p|person=3 51 51 ci ci _ _ _ _ _ arg2-ben
53 serveis servei servei n n postype=common|gen=m|num=p postype=common|gen=m|num=p 51 51 cd cd _ _ _ _ _ arg1-pat
54 particulars particular particular a a postype=qualificative|gen=c|num=p postype=qualificative|gen=c|num=p 53 53 s.a s.a _ _ _ _ _ _
55 . . . f f punct=period punct=period 7 7 f f _ _ _ _ _ _

The first sentence of the CoNLL 2009 development data:

1 Fundació_Privada_Fira_de_Manresa Fundació_Privada_Fira_de_Manresa Fundació_Privada_Fira_de_Manresa n n postype=proper|gen=c|num=c postype=proper|gen=c|num=c 3 3 suj suj _ _ arg0-agt
2 ha haver haver v v postype=auxiliary|gen=c|num=s|person=3|mood=indicative|tense=present postype=auxiliary|gen=c|num=s|person=3|mood=indicative|tense=present 3 3 v v _ _ _
3 fet fer fer v v postype=main|gen=m|num=s|mood=pastparticiple postype=main|gen=m|num=s|mood=pastparticiple 0 0 sentence sentence Y fer.a2 _
4 un un un d d postype=numeral|gen=m|num=s postype=numeral|gen=m|num=s 5 5 spec spec _ _ _
5 balanç balanç balanç n n postype=common|gen=m|num=s postype=common|gen=m|num=s 3 3 cd cd _ _ arg1-pat
6 de de de s s postype=preposition|gen=c|num=c postype=preposition|gen=c|num=c 5 5 sp sp _ _ _
7 l' el el d d postype=article|gen=c|num=s postype=article|gen=c|num=s 8 8 spec spec _ _ _
8 activitat activitat activitat n n postype=common|gen=f|num=s postype=common|gen=f|num=s 6 6 sn sn _ _ _
9 del del del s s postype=preposition|gen=m|num=s|contracted=yes postype=preposition|gen=m|num=s|contracted=yes 8 8 sp sp _ _ _
10 Palau_Firal Palau_Firal Palau_Firal n n postype=proper|gen=c|num=c postype=proper|gen=c|num=c 9 9 sn sn _ _ _
11 durant durant durant s s postype=preposition|gen=c|num=c postype=preposition|gen=c|num=c 8 3 sp cc _ _ _
12 els el el d d postype=article|gen=m|num=p postype=article|gen=m|num=p 15 15 spec spec _ _ _
13 primers primer primer a a postype=ordinal|gen=m|num=p postype=ordinal|gen=m|num=p 12 12 a a _ _ _
14 cinc cinc cinc d d postype=numeral|gen=c|num=p postype=numeral|gen=c|num=p 12 12 d d _ _ _
15 mesos mes mes n n postype=common|gen=m|num=p postype=common|gen=m|num=p 11 11 sn sn _ _ _
16 de de de s s postype=preposition|gen=c|num=c postype=preposition|gen=c|num=c 15 15 sp sp _ _ _
17 l' el el d d postype=article|gen=c|num=s postype=article|gen=c|num=s 18 18 spec spec _ _ _
18 any any any n n postype=common|gen=m|num=s postype=common|gen=m|num=s 16 16 sn sn _ _ _
19 . . . f f punct=period punct=period 3 3 f f _ _ _

The first sentence of the CoNLL 2009 test data:

1 El el el d d postype=article|gen=m|num=s postype=article|gen=m|num=s _ _ _ _ _
2 darrer darrer darrer a a postype=ordinal|gen=m|num=s postype=ordinal|gen=m|num=s _ _ _ _ _
3 número número número n n postype=common|gen=m|num=s postype=common|gen=m|num=s _ _ _ _ _
4 de de de s s postype=preposition|gen=c|num=c postype=preposition|gen=c|num=c _ _ _ _ _
5 l' el el d d postype=article|gen=c|num=s postype=article|gen=c|num=s _ _ _ _ _
6 Observatori_del_Mercat_de_Treball_d'_Osona Observatori_del_Mercat_de_Treball_d'_Osona Observatori_del_Mercat_de_Treball_d'_Osona n n postype=proper|gen=c|num=c postype=proper|gen=c|num=c _ _ _ _ _
7 inclou incloure incloure v v postype=main|gen=c|num=s|person=3|mood=indicative|tense=present postype=main|gen=c|num=s|person=3|mood=indicative|tense=present _ _ _ _ Y
8 un un un d d postype=numeral|gen=m|num=s postype=numeral|gen=m|num=s _ _ _ _ _
9 informe informe informe n n postype=common|gen=m|num=s postype=common|gen=m|num=s _ _ _ _ _
10 especial especial especial a a postype=qualificative|gen=c|num=s postype=qualificative|gen=c|num=s _ _ _ _ _
11 sobre sobre sobre s s postype=preposition|gen=c|num=c postype=preposition|gen=c|num=c _ _ _ _ _
12 la el el d d postype=article|gen=f|num=s postype=article|gen=f|num=s _ _ _ _ _
13 contractació contractació contractació n n postype=common|gen=f|num=s postype=common|gen=f|num=s _ _ _ _ _
14 a_través_de a_través_de a_través_de s s postype=preposition|gen=c|num=c postype=preposition|gen=c|num=c _ _ _ _ _
15 les el el d d postype=article|gen=f|num=p postype=article|gen=f|num=p _ _ _ _ _
16 empreses empresa empresa n n postype=common|gen=f|num=p postype=common|gen=f|num=p _ _ _ _ _
17 de de de s s postype=preposition|gen=c|num=c postype=preposition|gen=c|num=c _ _ _ _ _
18 treball treball treball n n postype=common|gen=m|num=s postype=common|gen=m|num=s _ _ _ _ _
19 temporal temporal temporal a a postype=qualificative|gen=c|num=s postype=qualificative|gen=c|num=s _ _ _ _ _
20 , , , f f punct=comma punct=comma _ _ _ _ _
21 les el el d d postype=article|gen=f|num=p postype=article|gen=f|num=p _ _ _ _ _
22 ETT ETT ETT n n postype=proper|gen=c|num=c postype=proper|gen=c|num=c _ _ _ _ _
23 . . . f f punct=period punct=period _ _ _ _ _

Parsing

Nonprojectivities in AnCora-CA are very rare. Only 487 of the 435,860 tokens in the CoNLL 2007 version are attached nonprojectively (0.11%). In the CoNLL 2009 version, there are no nonprojectivities at all.

The results of the CoNLL 2007 shared task are available online. They have been published in (Nivre et al., 2007). The evaluation procedure was changed to include punctuation tokens. These are the best results for Catalan:

Parser (Authors) LAS UAS
Titov et al. 87.40 93.40
Sagae 88.16 93.34
Malt (Nilsson et al.) 88.70 93.12
Nakagawa 87.90 92.86
Carreras 87.60 92.46
Malt (Hall et al.) 87.74 92.20

The two Malt parser results of 2007 (single malt and blended) are described in (Hall et al., 2007) and the details about the parser configuration are described here.

The results of the CoNLL 2009 shared task are available online. They have been published in (Hajič et al., 2009). Unlabeled attachment score was not published. These are the best results for Catalan:

Parser (Authors) LAS
Merlo 87.86
Che 86.56
Bohnet 86.35
Chen 85.88

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