A distinct feature of Hindu religious and philosophical text is that they
come from a library of texts rather than single source. The Upanishads is known
as one of the oldest philosophical texts in the world that forms the foundation
of Hindu philosophy. The Bhagavad Gita is core text of Hindu philosophy and is
known as a text that summarises the key philosophies of the Upanishads with
major focus on the philosophy of karma. These texts have been translated into
many languages and there exists studies about themes and topics that are
prominent; however, there is not much study of topic modelling using language
models which are powered by deep learning. In this paper, we use advanced
language produces such as BERT to provide topic modelling of the key texts of
the Upanishads and the Bhagavad Gita. We analyse the distinct and overlapping
topics amongst the texts and visualise the link of selected texts of the
Upanishads with Bhagavad Gita. Our results show a very high similarity between
the topics of these two texts with the mean cosine similarity of 73%. We find
that out of the fourteen topics extracted from the Bhagavad Gita, nine of them
have a cosine similarity of more than 70% with the topics of the Upanishads. We
also found that topics generated by the BERT-based models show very high
coherence as compared to that of conventional models. Our best performing model
gives a coherence score of 73% on the Bhagavad Gita and 69% on The Upanishads.
The visualization of the low dimensional embeddings of these texts shows very
clear overlapping among their topics adding another level of validation to our
results.

本文使用 BERT 等先进的语言模型对《奥义书》和《恒久之道》这两部核心印度教哲学文本进行主题建模，分析了它们之间的主题联系和重叠，结果显示两者的主题非常相似，并且 BERT 模型比传统模型的主题更为连贯，这从低维向量空间的可视化效果得到了佐证。