1 Dependency Parsing by Inference over High-recall Dependency Predictions Sander Canisius Toine Bogers Antal van den Bosch Jeroen Geertzen ILK / Language and Information Science Tilburg University Erik Tjong Kim Sang Informatics Institute University of Amsterdam
2 Highlights •No modelling, just classification •Simultaneously predicting and labelling dependency relations •Resolving inconsistencies on the basis of classifier confidence
3 ikhoorhaarzingen su obj1 vc Dependent Head Relation ik hoor SU ik haar - ik zingen -
4 ikhoorhaarzingen su obj1 vc Dependent Head Relation hoor ik - hoor haar - hoor zingen -
5 ikhoorhaarzingen su obj1 vc Dependent Head Relation haar ik - haar hoor OBJ1 haar zingen -
6 ikhoorhaarzingen su obj1 vc Dependent Head Relation zingen ik - zingen hoor VC zingen haar -
7 ik hoor haar zingen ik hoor haar zingen SU-hoor - OBJ1-hoor / DET-zingen VC-hoor
8 ik hoor haar zingen ik hoor haar zingen SU-hoor - OBJ1-hoor 0.8 / DET-zingen 0.5 VC-hoor
9 ik hoor haar zingen ik hoor haar zingen SU-hoor - OBJ1-hoor VC-hoor
10 Features •Head features –2-1-2 word & part-of-speech windows •Dependent features –2-1-2 word & part-of-speech windows •Relative position (LEFT / RIGHT) •Distance
11 •The relation prediction/classification has a highly skewed class distribution –Tends to result in high-precision, low-recall relation predictions •Down-sampling the negative class increases recall –(At the cost of precision)
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13 LanguageUASLAS Arabic Bulgarian Chinese Czech Danish Dutch German Japanese Portuguese Slovene Spanish Swedish Turkish Average