Estimating related words computationally using language model from the
  Mahabharata -- an Indian epic
Vrunda Gadesha Keyur D Joshi Shefali Naik
'Mahabharata' is the most popular among many Indian pieces of literature
referred to in many domains for completely different purposes. This text itself
is having various dimension and aspects which is useful for the human being in
their personal life and professional life. This Indian Epic is originally
written in the Sanskrit Language. Now in the era of Natural Language
Processing, Artificial Intelligence, Machine Learning, and Human-Computer
interaction this text can be processed according to the domain requirement. It
is interesting to process this text and get useful insights from Mahabharata.
The limitation of the humans while analyzing Mahabharata is that they always
have a sentiment aspect towards the story narrated by the author. Apart from
that, the human cannot memorize statistical or computational details, like
which two words are frequently coming in one sentence? What is the average
length of the sentences across the whole literature? Which word is the most
popular word across the text, what are the lemmas of the words used across the
sentences? Thus, in this paper, we propose an NLP pipeline to get some
statistical and computational insights along with the most relevant word
searching method from the largest epic 'Mahabharata'. We stacked the different
text-processing approaches to articulate the best results which can be further
used in the various domain where Mahabharata needs to be referred.