Normalized dataset for Sanskrit word segmentation and morphological parsing

Sanskrit processing has seen a surge in the use of data-driven approaches over the past decade. Various tasks such as segmentation, morphological parsing, and dependency analysis have been tackled through the development of state-of-the-art models despite working with relatively limited datasets compared to other languages. However, a significant challenge lies in the availability of annotated datasets that are lexically, morphologically, syntactically, and semantically tagged. While syntactic and semantic tags are preferable for later stages of processing such as sentential parsing and disambiguation, lexical and morphological tags are crucial for low-level tasks of word segmentation and morphological parsing. The Digital Corpus of Sanskrit (DCS) is one notable effort that hosts over 650,000 lexically and morphologically tagged sentences from around 250 texts but also comes with its limitations at different levels of a sentence like chunk, segment, stem and morphological analysis. To overcome these limitations, we look at alternatives such as Sanskrit Heritage Segmenter (SH) and Saṃsādhanī tools, that provide information complementing DCS’ data. This work focuses on enriching the DCS dataset by incorporating analyses from SH, thereby creating a dataset that is rich in lexical and morphological information. Furthermore, this work also discusses the impact of such datasets on the performances of existing segmenters, specifically the Sanskrit Heritage Segmenter.