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

only the lexicon is fine-grained to study the effect of the interaction between POS
tag and phrase structure. As seen, there is a positive interaction between them
which determines that simplifying the model and losing the detailed syntactic information
have a counter effect on parsing. Comparing Models 5 and 8 indicates
that reducing data sparsity results in a high performance. In Model 6, the lexicon
and the phrase structure are coarse-grained, while the POS tag is fine-grained.
This model is built to study the effect of available morpho-syntactic information
in case there is a reduction on data sparsity without the effect of the HPSG-based
annotation. The results of Models 2-4 infer that class-based parsing, the detailed
morpho-syntactic information in the POS tags, and the coarse representation of the
annotation at the phrasal level have positive impacts on parsing. The impact of
these three variables are represented together in Model 6 which outperforms all the
experimented models. In contrast, Model 3 which has an opposite configuration
performs the worst. In Model 7, the lexicon and the POS tag are coarse-grained
and the phrase structure is fine-grained to study the effect of the HPSG-based annotation
without the impact of the morpho-syntactic information but with less data
sparsity. Comparing Models 7 and 8 indicates the negative impact of the HPSGbased
annotation on parsing, since it is a hard task for the parser to determine the
type of dependencies when a coarse representation of the syntactic categories is
available. While a better performance is obtained when a finer representation of
the syntactic categories is available as determined in Models 2. Finally, in Model
8, the coarse-grained representations of the information at the three dimensions are
studied. Comparing Models 1 and 8 indicates that better results are obtained when
there is a coarse representation of linguistic knowledge, but higher results will be
obtained when, similar to Model 6, a richer POS tag is used.
The other observation on Table 1 is studying the effect of each annotation dimension
on all possible configurations. Comparing Models 2 and 1, Models 6
and 4, Models 7 and 3, and Models 8 and 5 show that the former models beat
the latter ones which indicates that the class-based model always outperforms the
word-based model disregarding the annotation of the POS tag and the phrase structure.
There can be a similar study on the effect of POS tag annotation by comparing
Models 1 and 3, Models 2 and 7, Models 4 and 5, and Models 6 and 8. All former
models outperform the latter ones which indicates the superiority of fine-grained
POS tag annotation disregarding the lexicon and phrase structure. To study the impact
of phrase structure annotation, Models 4 and 1, Models 5 and 3, Models 6 and
2, and Models 8 and 7 are compared. All former models perform better than the
latter ones which shows that the coarse-grained phrase structure annotation always
results in a higher parsing performance disregarding the lexicon and POS tag. It
has to be mentioned that the differences between the performance of all of the eight
models are statistically significant according to the 2-tailed t-test (p < 0.01).
4 Conclusion
In this paper, we studied the effect of annotation granularity on parsing from three
dimensions (lexicon, POS tag, and phrase structure) on Persian. Comparing the
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