Sanskrit Segmentation Revisited
This work addresses segmentation for Sanskrit computational analysis, but it is incremental as it builds on prior tools.
The paper tackles the problem of segmenting Sanskrit texts by modifying an existing segmenter to ignore phase details and introducing a probability function to prioritize solutions, resulting in improved ranking of valid segmentations.
Computationally analyzing Sanskrit texts requires proper segmentation in the initial stages. There have been various tools developed for Sanskrit text segmentation. Of these, Gérard Huet's Reader in the Sanskrit Heritage Engine analyzes the input text and segments it based on the word parameters - phases like iic, ifc, Pr, Subst, etc., and sandhi (or transition) that takes place at the end of a word with the initial part of the next word. And it enlists all the possible solutions differentiating them with the help of the phases. The phases and their analyses have their use in the domain of sentential parsers. In segmentation, though, they are not used beyond deciding whether the words formed with the phases are morphologically valid. This paper tries to modify the above segmenter by ignoring the phase details (except for a few cases), and also proposes a probability function to prioritize the list of solutions to bring up the most valid solutions at the top.