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This paper describes how we developed a neural-based dependency parser,namely ThamizhiUDp, which provides a complete pipeline for the dependencyparsing of the Tamil language text using Universal Dependency formalism. Wehave considered the phases of the dependency parsing pipeline and identifiedtools and resources in each of these phases to improve the accuracy and totackle data scarcity. ThamizhiUDp uses Stanza for tokenisation andlemmatisation, ThamizhiPOSt and ThamizhiMorph for generating Part of Speech(POS) and Morphological annotations, and uuparser with multilingual trainingfor dependency parsing. ThamizhiPOSt is our POS tagger, which is based on theStanza, trained with Amrita POS-tagged corpus. It is the currentstate-of-the-art in Tamil POS tagging with an F1 score of 93.27. Ourmorphological analyzer, ThamizhiMorph is a rule-based system with a very goodcoverage of Tamil. Our dependency parser ThamizhiUDp was trained usingmultilingual data. It shows a Labelled Assigned Score (LAS) of 62.39, 4 pointshigher than the current best achieved for Tamil dependency parsing. Therefore,we show that breaking up the dependency parsing pipeline to accommodateexisting tools and resources is a viable approach for low-resource languages.

ThamizhiUDp: A Dependency Parser for Tamil