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Table of Contents
Estonian (et)
Versions
- Downloadable on-line, part of Arborest project (puudepank)
- 8.12.2010 arborest.xml downloadable from the same site (same size, improved markup)
Obtaining and License
The EKP is freely downloadable from here in VISL or TIGER-XML format. Licensing terms are unknown.
EKP was created / coordinated (?) by Kaili Müürisep, Institute of Computer Science (Arvutiteaduse instituut), University of Tartu (Tartu Ülikool), Liivi 2, 50409 Tartu, Estonia.
References
- Website
- Data
- no separate citation
- Principal publications
- Kaili Müürisep, Tiina Puolakainen, Kadri Muischnek, Mare Koit, Tiit Roosmaa, Heli Uibo: A New Language for Constraint Grammar: Estonian. In: International Conference Recent Advances in Natural Language Processing. Proceedings, pp. 304-310, Borovets, Bulgaria, 2003.
- Documentation
- The header of the TIGER-XML version of the treebank contains lists of various sorts of tags with brief explanation.
Domain
Mixed:
- 388 tailored sentences with movement verbs
- 732 sentences with movement verbs from the Estonian FrameNet corpus
- 175 sentences from the Arborest corpus
- 20 sentences of spoken language
Size
According to their website, the TIGER Treebank version 1 contains approximately 700,000 tokens in 40,000 sentences. Version 2.1 contains approximately 900,000 tokens in 50,000 sentences.
The CoNLL 2006 version contains 705,304 tokens in 39573 sentences, yielding 17.82 tokens per sentence on average (CoNLL 2006 data split: 699,610 tokens / 39216 sentences training, 5694 tokens / 357 sentences test).
The CoNLL 2009 version contains 712,332 tokens in 40020 sentences, yielding 17.80 tokens per sentence on average (CoNLL 2009 data split: 648,677 tokens / 36020 sentences training, 32033 tokens / 2000 sentences development, 31622 tokens / 2000 sentences test).
Inside
The treebank is part of the Arborest project and VISL (Visual Interactive Syntax Learning). As such, it is based on Constraint Grammar (Fred Karlsson et al., 1995: Constraint Grammar – A Language-Independent System for Parsing Unrestricted Text. Mouton de Gruyter).
All versions contain semi-automatic part of speech tags (Stuttgart-Tübingen Tagset, STTS) and syntactic structure. Lemmas and morphosyntactic features are available only for newer versions (TIGER Treebank version 2 and onwards, and CoNLL 2009). The parts of speech are heavily context-dependent, e.g. many words can be used both substantively (pronouns) and attributively (determiners), which is distinguished by different POS tags.
It is not clear what the semi-automatic annotation means (probably first auto-tagging, then manual correction?) and whether it also applies to the morphosyntactic annotation. The CoNLL 2009 version also contains automatically disambiguated lemmas, tags and features.
The original treebank is phrase-based. The dependencies in the CoNLL versions must have thus been drawn using a head-selection procedure. Besides CoNLL data, the TIGER project also provides a subset of the TIGER Treebank in a dependency format.
Sample
The first sentence of TIGER Treebank 2.1 in the TIGER-XML format:
<s id="s1"> <graph root="s1_VROOT"> <terminals> <t id="s1_1" word="``" lemma="--" pos="$(" morph="--" case="--" number="--" gender="--" person="--" degree="--" tense="--" mood="--" /> <t id="s1_2" word="Ross" lemma="Ross" pos="NE" morph="Nom.Sg.Masc" case="Nom" number="Sg" gender="Masc" person="--" degree="--" tense="--" mood="--" /> <t id="s1_3" word="Perot" lemma="Perot" pos="NE" morph="Nom.Sg.Masc" case="Nom" number="Sg" gender="Masc" person="--" degree="--" tense="--" mood="--" /> <t id="s1_4" word="wäre" lemma="sein" pos="VAFIN" morph="3.Sg.Past.Subj" case="--" number="Sg" gender="--" person="3" degree="--" tense="Past" mood="Subj" /> <t id="s1_5" word="vielleicht" lemma="vielleicht" pos="ADV" morph="--" case="--" number="--" gender="--" person="--" degree="--" tense="--" mood="--" /> <t id="s1_6" word="ein" lemma="ein" pos="ART" morph="Nom.Sg.Masc" case="Nom" number="Sg" gender="Masc" person="--" degree="--" tense="--" mood="--" /> <t id="s1_7" word="prächtiger" lemma="prächtig" pos="ADJA" morph="Pos.Nom.Sg.Masc" case="Nom" number="Sg" gender="Masc" person="--" degree="Pos" tense="--" mood="--" /> <t id="s1_8" word="Diktator" lemma="Diktator" pos="NN" morph="Nom.Sg.Masc" case="Nom" number="Sg" gender="Masc" person="--" degree="--" tense="--" mood="--" /> <t id="s1_9" word="''" lemma="--" pos="$(" morph="--" case="--" number="--" gender="--" person="--" degree="--" tense="--" mood="--" /> </terminals> <nonterminals> <nt id="s1_500" cat="PN"> <edge label="PNC" idref="s1_2" /> <edge label="PNC" idref="s1_3" /> </nt> <nt id="s1_501" cat="NP"> <edge label="NK" idref="s1_6" /> <edge label="NK" idref="s1_7" /> <edge label="NK" idref="s1_8" /> </nt> <nt id="s1_502" cat="S"> <edge label="SB" idref="s1_500" /> <edge label="HD" idref="s1_4" /> <edge label="MO" idref="s1_5" /> <edge label="PD" idref="s1_501" /> </nt> <nt id="s1_VROOT" cat="VROOT"> <edge label="--" idref="s1_1" /> <edge label="--" idref="s1_502" /> <edge label="--" idref="s1_9" /> </nt> </nonterminals> </graph> </s>
The first sentence of the CoNLL 2006 training data:
| 1 | `` | _ | $( | $( | _ | 4 | PUNC | 4 | PUNC |
| 2 | Ross | _ | NE | NE | _ | 4 | SB | 4 | SB |
| 3 | Perot | _ | NE | NE | _ | 2 | PNC | 2 | PNC |
| 4 | wäre | _ | VAFIN | VAFIN | _ | 0 | ROOT | 0 | ROOT |
| 5 | vielleicht | _ | ADV | ADV | _ | 4 | MO | 4 | MO |
| 6 | ein | _ | ART | ART | _ | 8 | NK | 8 | NK |
| 7 | prächtiger | _ | ADJA | ADJA | _ | 8 | NK | 8 | NK |
| 8 | Diktator | _ | NN | NN | _ | 4 | PD | 4 | PD |
| 9 | '' | _ | $( | $( | _ | 4 | PUNC | 4 | PUNC |
The first sentence of the CoNLL 2006 test data:
| 1 | Zwei | _ | CARD | CARD | _ | 2 | NK | 2 | NK |
| 2 | Themen | _ | NN | NN | _ | 14 | SB | 14 | SB |
| 3 | , | _ | $, | $, | _ | 2 | PUNC | 2 | PUNC |
| 4 | die | _ | PRELS | PRELS | _ | 8 | OA | 8 | OA |
| 5 | Perot | _ | NE | NE | _ | 8 | SB | 8 | SB |
| 6 | immer | _ | ADV | ADV | _ | 7 | MO | 7 | MO |
| 7 | wieder | _ | ADV | ADV | _ | 8 | MO | 8 | MO |
| 8 | anspricht | _ | VVFIN | VVFIN | _ | 2 | RC | 2 | RC |
| 9 | , | _ | $, | $, | _ | 2 | PUNC | 2 | PUNC |
| 10 | Rezession | _ | NN | NN | _ | 2 | APP | 2 | APP |
| 11 | und | _ | KON | KON | _ | 10 | CD | 10 | CD |
| 12 | Bürokratie | _ | NN | NN | _ | 10 | CJ | 10 | CJ |
| 13 | , | _ | $, | $, | _ | 14 | PUNC | 14 | PUNC |
| 14 | machen | _ | VVFIN | VVFIN | _ | 0 | ROOT | 0 | ROOT |
| 15 | ihnen | _ | PPER | PPER | _ | 18 | DA | 18 | DA |
| 16 | besonders | _ | ADV | ADV | _ | 18 | MO | 18 | MO |
| 17 | zu | _ | PTKZU | PTKZU | _ | 18 | PM | 18 | PM |
| 18 | schaffen | _ | VVINF | VVINF | _ | 14 | OC | 14 | OC |
| 19 | . | _ | $. | $. | _ | 14 | PUNC | 14 | PUNC |
The first sentence of the CoNLL 2009 training data:
| 1 | `` | _ | `` | $( | $( | _ | _ | 4 | 4 | PUNC | PUNC | _ | _ |
| 2 | Ross | Ross | Roß | NE | NN | Nom|Sg|Masc | _ | 3 | 3 | PNC | PNC | _ | _ |
| 3 | Perot | Perot | Perot | NE | NE | Nom|Sg|Masc | _ | 4 | 4 | SB | SB | _ | _ |
| 4 | wäre | sein | sein | VAFIN | VAFIN | 3|Sg|Past|Subj | *|Sg|Past|Subj | 0 | 0 | ROOT | ROOT | _ | _ |
| 5 | vielleicht | vielleicht | vielleicht | ADV | ADV | _ | _ | 4 | 4 | MO | MO | _ | _ |
| 6 | ein | ein | ein | ART | ART | Nom|Sg|Masc | *|Sg|* | 8 | 8 | NK | NK | _ | _ |
| 7 | prächtiger | prächtig | prächtig | ADJA | ADJA | Pos|Nom|Sg|Masc | *|*|*|* | 8 | 8 | NK | NK | _ | _ |
| 8 | Diktator | Diktator | Diktator | NN | NN | Nom|Sg|Masc | *|Sg|Masc | 4 | 4 | PD | PD | _ | _ |
| 9 | '' | _ | '' | $( | $( | _ | _ | 4 | 4 | PUNC | PUNC | _ | _ |
The first sentence of the CoNLL 2009 development data:
| 1 | Maschinenbau | Maschinenbau | Maschinenbau | NN | NN | Nom|Sg|Masc | *|Sg|Masc | 0 | 4 | ROOT | NK | _ | _ |
| 2 | / | _ | / | $( | $( | _ | _ | 0 | 1 | PUNC | PUNC | _ | _ |
| 3 | ( | _ | ( | $( | $( | _ | _ | 0 | 4 | PUNC | PUNC | _ | _ |
| 4 | Zusammenfassung | Zusammenfassung | Zusammenfassung | NN | NN | Nom|Sg|Fem | *|Sg|Fem | 0 | 0 | ROOT | ROOT | _ | _ |
| 5 | ) | _ | ) | $( | $( | _ | _ | 0 | 1 | PUNC | PUNC | _ | _ |
The first sentence of the CoNLL 2009 test data:
| 1 | Gegen | gegen | gegen | APPR | APPR | _ | _ | _ | _ | _ | _ | _ |
| 2 | eine | ein | ein | ART | ART | Acc|Sg|Fem | *|Sg|Fem | _ | _ | _ | _ | _ |
| 3 | Erweiterung | Erweiterung | Erweiterung | NN | NN | Acc|Sg|Fem | *|Sg|Fem | _ | _ | _ | _ | _ |
| 4 | ihrer | ihr | ihr | PPOSAT | PPOSAT | Gen|Sg|Fem | *|*|* | _ | _ | _ | _ | _ |
| 5 | Organisation | Organisation | Organisation | NN | NN | Gen|Sg|Fem | *|Sg|Fem | _ | _ | _ | _ | _ |
| 6 | zu | zu | zu | APPR | APPR | _ | _ | _ | _ | _ | _ | _ |
| 7 | einem | ein | ein | ART | ART | Dat|Sg|Neut | Dat|Sg|* | _ | _ | _ | _ | _ |
| 8 | sicherheitspolitischen | sicherheitspolitisch | sicherheitspolitisch | ADJA | ADJA | Pos|Dat|Sg|Neut | Pos|*|*|* | _ | _ | _ | _ | _ |
| 9 | Forum | Forum | Forum | NN | NN | Dat|Sg|Neut | *|Sg|Neut | _ | _ | _ | _ | _ |
| 10 | sprachen | sprechen | sprechen | VVFIN | VVFIN | 3|Pl|Past|Ind | *|Pl|Past|Ind | _ | _ | _ | _ | Y |
| 11 | sich | sich | er|es|sie|Sie | PRF | PRF | 3|Acc|Pl | *|*|* | _ | _ | _ | _ | _ |
| 12 | die | der | d | ART | ART | Nom|Pl|Masc | *|*|* | _ | _ | _ | _ | _ |
| 13 | meisten | meister | meist | PIAT | PIAT | Nom|Pl|Masc | *|*|* | _ | _ | _ | _ | _ |
| 14 | Staaten | Staat | Staat | NN | NN | Nom|Pl|Masc | *|Pl|Masc | _ | _ | _ | _ | _ |
| 15 | beim | bei | beim | APPRART | APPRART | Dat|Sg|Neut | Dat|Sg|* | _ | _ | _ | _ | _ |
| 16 | Gipfeltreffen | Gipfeltreffen | Gipfeltreffen | NN | NN | Dat|Sg|Neut | *|*|Neut | _ | _ | _ | _ | _ |
| 17 | für | für | für | APPR | APPR | _ | _ | _ | _ | _ | _ | _ |
| 18 | Asiatisch-Pazifische | asiatisch-pazifisch | Asiatisch-Pazifische | ADJA | NN | Pos|Acc|Sg|Fem | *|*|* | _ | _ | _ | _ | _ |
| 19 | Wirtschaftskooperation | Wirtschaftskooperation | Wirtschaftskooperation | NN | NN | Acc|Sg|Fem | *|Sg|Fem | _ | _ | _ | _ | _ |
| 20 | ( | _ | ( | $( | $( | _ | _ | _ | _ | _ | _ | _ |
| 21 | Apec | Apec | _ | NE | NE | Nom|Sg|Fem | _ | _ | _ | _ | _ | _ |
| 22 | ) | _ | ) | $( | $( | _ | _ | _ | _ | _ | _ | _ |
| 23 | in | in | in | APPR | APPR | _ | _ | _ | _ | _ | _ | _ |
| 24 | Osaka | Osaka | Osaka | NE | NE | Dat|Sg|Neut | *|Sg|Neut | _ | _ | _ | _ | _ |
| 25 | aus | aus | aus | PTKVZ | PTKVZ | _ | _ | _ | _ | _ | _ | _ |
| 26 | . | _ | . | $. | $. | _ | _ | _ | _ | _ | _ | _ |
Parsing
TIGER is a mildly nonprojective treebank. 15875 of the 680,710 tokens in the CoNLL 2009 training+development datasets are attached nonprojectively (2.33%).
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 German:
| Parser (Authors) | LAS | UAS |
|---|---|---|
| MST (McDonald et al.) | 87.34 | 90.38 |
| Riedel et al. | 86.24 | 89.76 |
| Basis (O'Neil) | 85.36 | 89.16 |
| Malt (Nivre et al.) | 85.82 | 88.76 |
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 German:
| Parser (Authors) | LAS |
|---|---|
| Bohnet | 87.48 |
| Merlo | 87.29 |
| Chen | 86.24 |
| Che | 86.19 |