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An Empirical Study of Differences between Conversion Schemes and Annotation Guidelines

What it is about

Many dependency treebanks are converted from constituency treebanks, and different conversion schemes make different choices about heads and relation labels. The author measures how far such schemes differ with a generalized tree-edit distance, and compares them with learnability and with downstream performance. Experiments convert the Penn Treebank with four schemes (Yamada, CoNLL, Stanford and LTH). Parser accuracy on section 23 differs clearly for the Stanford scheme (LAS 81.36) against 87.52 to 88.99 for the others, but the parser's own bias makes many differences invisible to the parser. Learnability of a scheme does not predict its downstream score, but the choice of scheme does change the results of downstream applications.

Ideas

For Mova

The conversions are in en/src/convert.rs and the dialect in The Mova dialect: a UD metadialect, treebanks, and converters, the silver checks in tools/treeagree and the witnesses in tools/parsecmp. The chapter that the ideas touch is 20. Syntax: the Universal Dependencies frame. Measure first: the LAS spread between two conversions of the same EWT trees.

Topics: Treebanks

Cite

The paper itself, not this card. BibTeX from the library's record of the work.

@inproceedings{sogaard2013empirical,
  title = {{An Empirical Study of Differences between Conversion Schemes and Annotation Guidelines}},
  author = {Søgaard, Anders},
  booktitle = {Proceedings of the Second International Conference on Dependency Linguistics (DepLing 2013)},
  year = {2013},
  url = {https://aclanthology.org/W13-3733/}
}

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