A Treebank-based Investigation of IPP-triggering Verbs in Dutch

mance is not inconsiderable, as was the case for Talbanken and NDT. However, as
evidenced by the DDT-NDT pair, the labelled performance varies more in function
of the characteristics of the source treebank.
Finally, it turns out that delexicalisation is not required if the languages in question
are closely enough related that there is non-trivial amounts of lexical overlap
between the languages. But if a delexicalised parser is required and the source
treebank is large, it may be best to not use the entire treebank to train the adapted
parser. As the learning curves in Figure 4 show, a delexicalised parser trained on
more than 500-1,000 sentences starts to overtrain and is likely to perform worse
than a parser trained on less material on target language data.
5 Future work
There are a number of topics that merit further investigation. First of all, a thorough
study is required of the influence of using pre-parsed data for annotation and the
influence of parser performance on the parameters of annotation process, such as
time used per sentence and inter-annotator agreement. Fort and Sagot [3] study the
influence of pre-processed data for the part-of-speech annotation task, and Chiou
et al. [2] show that an accurate parser speeds up phrase structure annotation, but to
our knowledge no such study has been done for the task of dependency annotation.
Given languages as similar as the languages used here, it would also be quite
interesting to discard the notion of three distinct languages, and rather consider
them three dialects of the same language and apply orthographic normalisation
techniques to automatically convert Swedish or Danish data to Norwegian.
Finally comes the question of parameter tuning. In an in-domain setting techniques
such as cross-validation over the training set are useful techniques to find the
best set of parameters for an algorithm on a data-set, but for cross-lingual parsing, it
is not at all obvious that the model that performs optimally on the source-language
data is the best model for the target domain. It is thus necessary to further investigate
the possible correlation between source and target domain performance and
methods of a priori estimation of performance on out-of-domain data.
References
[1] Charniak, E. and Johnson, M. (2005). Coarse-to-Fine N-Best Parsing and Max-
Ent Discriminative Reranking. In Proceedings of the 43rd Annual Meeting of
the Association for Computational Linguistics (ACL’05), pages 173–180, Ann
Arbor, Michigan. Association for Computational Linguistics.
[2] Chiou, F.-D., Chiang, D., and Palmer, M. (2001). Facilitating Treebank Annotation
Using a Statistical Parser. In Proceedings of the first international
conference on Human language technology research, pages 1–4.
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