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

obtained with MALTParser is 76.61% label attachment score, 88.45% unlabeled attachment
score, 80.03% label accuracy score. The best result for both the parsers is
obtained on the same experimental settings incorporating case information, which
further strengthens our conjecture on the importance of case markers. All the experiments
suggest the effectiveness of the features successively included in the
system. By incorporating the POS-tag, chunk tag, lemma and case markers both
the parsers have shown a steep increase in their performance. However, GNP information
had a negative impact on the performance of both the parsers. In Urdu
verb mainly agrees with its nominative argument which can either be an ‘agent’
or a ‘theme’. In sentences where both the arguments of a verb (transitive) are in
nominative form, it agrees with ‘agent’. A verb can also agree with a constituent
inside its clausal complement, the phenomenon called Long Distance Agreement.
It’s probably because of this agreement behavior of a verb which affects the parsing
accuracy. Among all the features incorporated, case markers have played a
major role in improving the parsing performance. A 10.74% increment in LAS in
MaltParser with case information makes a clear statement about the importance of
morphology based parsing of MRLs.
During the error analysis, the primary confusion is observed due to granularity
of the tag-set, for labels ‘r6’ and ‘r6-k2’ (genitive relations) for example a total of
50 cases have been interchangeably marked incorrect. This can be explained by the
fact that the labels differ slightly in their semantics and it is potentially not possible
to disambiguate based on the simple features used in the experiments. The issue of
granularity will automatically subside if a coarse grained tag-set is used ignoring
finer distinctions.
5 Conclusion and Future Work
This paper presents our efforts towards the dependency parsing of Urdu. It is an
attempt to explore the importance of linguistic information encoded in the morphology
of the language for data driven parsing. Our main inference from the
experiments is that some morphological features viz. the case markers play a vital
role in parsing while on the other hand morphological information of gender,
number and person (agreement features), has not delivered any improvement. In
the process we have also investigated the extent of impact of non-projectivity, and
inspected the role of POS tags and lemma on parsing accuracy.
We have currently reported our work on the chunk heads, which we wish to
extend to full parsing with chunks expanded along with their intra-chunk dependencies.
Also the current dependency tag-set is highly fine grained, consisting of
53 tags, and often for practical applications we do not need such deep analysis.
So, our efforts would be to come up with an efficient parser with coarse grained
dependency labels.
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