  This paper describes how we developed a neural-based dependency parser,
namely ThamizhiUDp, which provides a complete pipeline for the dependency
parsing of the Tamil language text using Universal Dependency formalism. We
have considered the phases of the dependency parsing pipeline and identified
tools and resources in each of these phases to improve the accuracy and to
tackle data scarcity. ThamizhiUDp uses Stanza for tokenisation and
lemmatisation, ThamizhiPOSt and ThamizhiMorph for generating Part of Speech
(POS) and Morphological annotations, and uuparser with multilingual training
for dependency parsing. ThamizhiPOSt is our POS tagger, which is based on the
Stanza, trained with Amrita POS-tagged corpus. It is the current
state-of-the-art in Tamil POS tagging with an F1 score of 93.27. Our
morphological analyzer, ThamizhiMorph is a rule-based system with a very good
coverage of Tamil. Our dependency parser ThamizhiUDp was trained using
multilingual data. It shows a Labelled Assigned Score (LAS) of 62.39, 4 points
higher than the current best achieved for Tamil dependency parsing. Therefore,
we show that breaking up the dependency parsing pipeline to accommodate
existing tools and resources is a viable approach for low-resource languages.
