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

The Effect of Treebank Annotation Granularity on
Parsing: A Comparative Study
Masood Ghayoomi
Freie Universität Berlin
E-mail: masood.ghayoomi@fu-berlin.de
Omid Moradiannasab
Iran University of Science and Technology
E-mail: omidmoradiannasab@gmail.com
Abstract
Statistical parsers need annotated data for training. Depending on the available
linguistic information in the training data, the performance of the parsers
vary. In this paper, we study the effect of annotation granularity on parsing
from three points of views: lexicon, part-of-speech tag, and phrase structure.
The results show that changing annotation granularity at each of these
dimensions has a significant impact on parsing performance.
1 Introduction
Parsing is one of the main tasks in Natural Language Processing (NLP). The stateof-the-art
statistical parsers are trained with treebanks [4, 5], mainly developed
based on the Phrase Structure Grammar (PSG). The part-of-speech (POS) tags of
the words in the treebanks are defined according to a tag set which contains the
syntactic categories of the words with the optional additional morpho-syntactic information.
Moreover, the constituent labels in treebanks might also be enriched
with syntactic functions. The developed annotated data in the framework of deeper
formalisms such as HPSG [13] has provided a fine representation of linguistic
knowledge. The performance of the parsers trained with the latter data set have not
beaten the state-of-the-art results [12] which shows that fine-grained representation
of linguistic knowledge adds complexities to a parser and it has a counter-effect on
the performance of the parser. In this paper, we aim to comprehensively study the
effect of annotation granularity on parsing from three points of views: lexicon,
POS tag, and phrase structure. This study has a different perspective than Rehbein
and van Genabith [14]. We selected Persian, a language from the Indo-European
language family, as the target language of our study.
2 Treebank Annotation Dimensions
Lexicon: The words of a language represent fine-grained concepts, and the linguistic
information added to the words plays a very important role for lexicalized,
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