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CoNLL-X Shared Task on Multilingual Dependency Parsing

CoNLL-X Shared Task on Multilingual Dependency Parsing. Sabine Buchholz, Speech Technology Group, Cambridge Research Lab, Toshiba Research Europe Ltd, UK Erwin Marsi, Tilburg University, The Netherlands Amit Dubey, University of Edinburgh, UK Yuval Krymolowski, University of Haifa, Israel.

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CoNLL-X Shared Task on Multilingual Dependency Parsing

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  1. CoNLL-X Shared Task on Multilingual Dependency Parsing Sabine Buchholz, Speech Technology Group, Cambridge Research Lab, Toshiba Research Europe Ltd, UK Erwin Marsi, Tilburg University, The Netherlands Amit Dubey, University of Edinburgh, UK Yuval Krymolowski, University of Haifa, Israel

  2. Overview • Introduction • Dependency structures • Data format • Treebanks used • Evaluation metric • Parsing approaches • Conclusion • Results • Analysis • The Future

  3. punc ROOT comp subj det This is a test . Dependency structures • No constituents (unlike phrase structure) • Dependency relations between two lexical items (tokens) • One possible graphical representation:

  4. Dependency structure — terminology • Child • Dependent • Modifier • Label subj This is • Parent • Governor • Head Note: Other people may draw arrows from head to child!

  5. Dependency structures — in the shared task punc ROOT comp • Virtual root node • Each token except BOS has exactly one head • More than one token can link to BOS • Crossing arcs are allowed, i.e. structures can be non-projective subj det BOS This is a test . 0 1 2 3 4 5 Do you need it for something ? What do you need it for ? An arc (i,j) is projective iff all nodes occurring between i and j are dominated by i (where dominates is the transitive closure of the arc relation)

  6. punc Data format ROOT comp subj det BOS This is a test . 0 1 2 3 4 5

  7. Data format — details • ID, FORM, CPOSTAG, POSTAG, HEAD and DEPREL guaranteed to contain a non-dummy value • except Spanish DEPREL bug ...  • Although CPOSTAG and POSTAG may be identical • German and Swedish • LEMMA and FEATS allowed to contain dummy value ( _ ) • if information not available in original treebank • Additional columns PHEAD and PDEPREL (in training data) not used by anybody • Unicode (UTF-8)

  8. Treebanks used • Czech: Prague Dependency Treebank (PDT) • Arabic: Prague Arabic Dependency Treebank (PADT) • Slovene: Slovene Dependency Treebank (SDT) • Danish: Danish Dependency Treebank (DDT) • Swedish: Talbanken05 • Turkish: Metu-Sabancı treebank • German: TIGER treebank • Japanese: Japanese Verbmobil treebank • Portuguese: The Bosque part of the Floresta sintá(c)tica • Dutch: Alpino treebank • Chinese: Sinica treebank • Spanish: Cast3LB • Bulgarian: BulTreeBank Depen- dency format Consti- tuents and functions Constituents andsome functions

  9. Data format — some examples #5:5.[39031] VP(evaluation:Dbb:也|Head:V_11:是|range:NP(property:Nv3:同班|Head:Nab:同學))#。(PERIODCATEGORY)

  10. [#1,AuxS,tag=HEADLINE,#1,ord=0,comment=Sun Oct 3 05:02:28 2004 \[SyntaxFS.pl 1.06\],x_id_ord=#1_1, x_comment=ALH20010911.0001_story Wed Jul 21 12:51:09 2004 \[MorphoFS.pl 1.09\]]\(\[غِيابُ,ExD,غِياب_1,N-------1R,غياب,ord=1,x_id_ord=#1/1_12, x_lookup=gyAb,giyAb+u,absence/disappearance + \[def.nom.\]]\(\[كَنْعان,Atr,كَنْعان_2,Z---------,كنعان,ord=3,x_id_ord=#1/3_6, x_lookup=knEAn,kanoEAn,Kan'an]\(\[فُؤاد,Atr,فُؤاد_2,Z---------,فؤاد,ord=2,x_id_ord=#1/2_11, x_lookup=f&Ad,fu&Ad,Fuad/Fouad]\)))

  11. <node id="0" rel="top" cat="top" begin="0" end="6"> <node id="1" rel="--" cat="sv1" begin="0" end="5"> <node id="2" rel="hd" pos="verb" begin="0" end="1" root="ben" word="Ben"/> <node id="3" rel="su" pos="noun" begin="1" end="2" root="je" word="je"/> <node id="4" rel="predc" cat="mwu" begin="2" end="5"> <node id="5" rel="mwp" pos="adj" begin="2" end="3" root="op" word="op"/> <node id="6" rel="mwp" pos="adj" begin="3" end="4" root="de" word="de"/> <node id="7" rel="mwp" pos="adj" begin="4" end="5" root="hoogte" word="hoogte"/> </node> </node> <node id="8" rel="--" pos="punct" begin="5" end="6" root="?" word="?"/> </node> <sentence>Ben je op de hoogte ?</sentence>

  12. <S No="2"> <W IX="1" LEM="" MORPH=" " IG='[(1,"kurtul+Verb+Pos")(2,"Noun+Inf+A3sg+Pnon+Nom")]' REL="[2,1,(OBJECT)]"> Kurtulmak </W> <W IX="2" LEM="" MORPH=" " IG='[(1,"iste+Verb+Neg+Prog1+A1sg")]' REL="[3,1,(SENTENCE)]"> istemiyorum </W> <W IX="3" LEM="" MORPH=" " IG='[(1,".+Punc")]' REL="[,( )]"> . </W> </S>

  13. SOURCE: CETEMPúblico n=22 sec=eco sem=92a CP22-4 Mas se falhar? A1 UTT:acl =CO:conj-c('mas') Mas =ADVL:fcl ==SUB:conj-s('se') se ==P:v-fin('falhar' FUT 3S SUBJ) falhar =? Head table • acl: COM, PRD, P, leftmost non-punctuation • fcl: P, PAUX, …

  14. Data format — training data and test data • Training data • Contains all columns • “Blind” test data (given to participants) • Contains only first six columns • Participants predict: HEAD and DEPREL • Approximately 5000 “scoring” tokens for each language

  15. Evaluation metric • Official metric: Labelled attachment score (LAS) • The percentage of “scoring” token for which the system predicted the correct HEAD and DEPREL value • A token is “non-scoring” if all characters of the FORM value have the Unicode category property “Punctuation” • E.g. “.” “,” “?” “(“ “¿” “…” “--” “_” “؟_?” “%” … • Also computed, for error analysis and system comparison: • Unlabelled attachment score (UAS) • The percentage of “scoring” token for which the system predicted the correct HEAD value • Label accuracy • The percentage of “scoring” token for which the system predicted the correct DEPREL value

  16. Parsing approaches • Many different approaches! • How to deal with non-projectivity • Different machine learners • perceptron, Maximum Entropy, SVM, MLE, ... • Search • deterministic, n-best, approximate, optimal, ... • FORM versus LEMMA, CPOSTAG versus POSTAG • Always use one, always use both, one or the other, ... • FEATS • Ignore, treat as atomic, split into components, cross-product, ... • Unlabelled parsing (HEAD) versus labelling (DEPREL) • Interleaved or separate step

  17. Parsing approaches — Parsing order • Four clusters (1 “long” + several “spotlight” talks) of talks today • “All pairs” (cluster 4) versus “stepwise (clusters 1– 3) • Chart-parsing (most of cluster 3) versus classifier-based (1+2) • Child-focused (cluster 1) versus direction-focused (cluster 2)

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