Validation and Normalization of DCS corpus using Sanskrit Heritage tools to build a tagged Gold Corpus
This work provides a standardized gold corpus for Sanskrit computational linguistics, but it is incremental as it builds on prior alignment efforts.
The paper addresses inconsistencies in the Digital Corpus of Sanskrit by standardizing and validating it using the Sanskrit Heritage Engine, resulting in an updated alignment process that records additional linguistic differences to build a tagged gold corpus.
The Digital Corpus of Sanskrit records around 650,000 sentences along with their morphological and lexical tagging. But inconsistencies in morphological analysis, and in providing crucial information like the segmented word, urges the need for standardization and validation of this corpus. Automating the validation process requires efficient analyzers which also provide the missing information. The Sanskrit Heritage Engine's Reader produces all possible segmentations with morphological and lexical analyses. Aligning these systems would help us in recording the linguistic differences, which can be used to update these systems to produce standardized results and will also provide a Gold corpus tagged with complete morphological and lexical information along with the segmented words. Krishna et al. (2017) aligned 115,000 sentences, considering some of the linguistic differences. As both these systems have evolved significantly, the alignment is done again considering all the remaining linguistic differences between these systems. This paper describes the modified alignment process in detail and records the additional linguistic differences observed. Reference: Amrith Krishna, Pavankumar Satuluri, and Pawan Goyal. 2017. A dataset for Sanskrit word segmentation. In Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, page 105-114. Association for Computational Linguistics, August.