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

statistical parsers. Since data sparsity is the biggest challenge in data oriented
parsing, parsers will have a poor performance if they are trained with a small set
of data, or when the domain of the training and the test data are not similar. Brown
et al. [2] pioneered to use word clustering for language modeling methods. Later
on, word clustering was widely used in various NLP applications including parsing
[3, 7, 9]. The Brown word clustering is an approach which provides a coarse
level of word representation such that the words which have similarities with each
other are assigned to one cluster. In this type of annotation, instead of words, the
corresponding mapped clusters are used.
POS Tag: The syntactic functions of words at the sentence level are the very
basic linguistic knowledge that the parser learns; therefore, they play a very important
role on the performance of a parser. The quality of the assigned POS
tags to the words and the amount of information they contain have a direct effect
on the performance of the parser. The representation of this knowledge can be
either coarse-grained such as Noun, Verb, Adjective, etc, or fine-grained to contain
morpho-syntactic and semantic information such as Noun-Single, Verb-Past,
Adjective-superlative, etc. The fine representation of the tags leads to increase the
tag set size and intensify the complexity of the tagging task for a statistical POS
tagger to disambiguate the correct labels.
Phrase Structure: Depending on the formalism used as the backbone of a treebank,
the labels of the nodes at the phrasal level can be either fine- or coarsegrained.
The annotated data in the Penn Treebank [11] provides relatively coarsegrained
phrase structures in which mostly the types of the phrasal constituents such
as NP, VP, etc are determined. It is not denied that in the latest version of the Penn
Treebank the syntactic functions are added to the labels as well, but this information
is not available for all nodes. Contrary, annotated data of deep formalisms like
HPSG provides a fine representation of phrase structures since the types of headdaughters’
dependencies are defined explicitly for all nodes such as the Bulgarian
treebank [15] and the Persian treebank [6]. Representation of dependency information
on constituents adds complexities to the parser for disambiguating the type
of the dependencies as the size of the constituent label set is increased.
3 Evaluation
3.1 Data Set and Tool
The Bijankhan Corpus 1 contains more than 2.5 million word tokens, and it is
POS tagged manually with a rich set of 586 tags containing morpho-syntactic and
semantic information [1] such that there is a hierarchy on the assigned tags based
on the EAGLES guidelines [10]. The number of the main syntactic categories in
the tag set is 14, and it is increased to 15 when clitics are splited from their hosts.
The Persian Treebank (PerTreeBank) 2 is a treebank for Persian developed in the
framework of HPSG [13] such that the basic properties of HPSG are simulated
1 http://ece.ut.ac.ir/dbrg/bijankhan/
2 http://hpsg.fu-berlin.de/∼ghayoomi/PTB.html
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