  Recently, the supervised learning paradigm's surprisingly remarkable
performance has garnered considerable attention from Sanskrit Computational
Linguists. As a result, the Sanskrit community has put laudable efforts to
build task-specific labeled data for various downstream Natural Language
Processing (NLP) tasks. The primary component of these approaches comes from
representations of word embeddings. Word embedding helps to transfer knowledge
learned from readily available unlabelled data for improving task-specific
performance in low-resource setting. Last decade, there has been much
excitement in the field of digitization of Sanskrit. To effectively use such
readily available resources, it is very much essential to perform a systematic
study on word embedding approaches for the Sanskrit language. In this work, we
investigate the effectiveness of word embeddings. We classify word embeddings
in broad categories to facilitate systematic experimentation and evaluate them
on four intrinsic tasks. We investigate the efficacy of embeddings approaches
(originally proposed for languages other than Sanskrit) for Sanskrit along with
various challenges posed by language.
