mlsanityData to 7 Oct 2026

Artificial intelligence for topic modelling in Hindu philosophy: mapping themes between the Upanishads and the Bhagavad Gita

arXiv 2205.11020 ↗First posted 23 May 2022cs.CLcited by 6

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Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 34 of its 186 references that are in the map. First posted on arXiv 23 May 2022.

Builds on

34 of 186 references.

References · 34

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  6. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  7. 2020Longformer: The Long-Document Transformercited by 3,362
  8. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,841
  9. 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,833
  10. 2019ALBERT: A Lite BERT for Self-supervised Learning of Language Representationscited by 2,772
  11. 2015Character-level Convolutional Networks for Text Classificationcited by 2,293
  12. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,935
22 more
  1. 2019SciBERT: A Pretrained Language Model for Scientific Textcited by 1,404
  2. 2022BERTopic: Neural topic modeling with a class-based TF-IDF procedurecited by 779
  3. 2021Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategiescited by 736
  4. 2018Universal Sentence Encodercited by 485
  5. 2020Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease predictioncited by 368
  6. 2015LSTM: A Search Space Odysseycited by 315
  7. 2019Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approachcited by 299
  8. 2016LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detectioncited by 213
  9. 2020Top2Vec: Distributed Representations of Topicscited by 124
  10. 2020LEGAL-BERT: The Muppets straight out of Law Schoolcited by 111
  11. 2017Accelerated Hierarchical Density Clusteringcited by 100
  12. 2020COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twittercited by 96
  13. 2020Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!cited by 73
  14. 2019Exploiting BERT for End-to-End Aspect-based Sentiment Analysiscited by 57
  15. 2020QED: A Framework and Dataset for Explanations in Question Answeringcited by 55
  16. 2019Towards Making the Most of BERT in Neural Machine Translationcited by 43
  17. 2020Topic Modeling with Contextualized Word Representation Clusterscited by 41
  18. 2019On the use of BERT for Neural Machine Translationcited by 23
  19. 2020Context-Guided BERT for Targeted Aspect-Based Sentiment Analysiscited by 18
  20. 2021COVID-19 sentiment analysis via deep learning during the rise of novel casescited by 18
  21. 2022Semantic and sentiment analysis of selected Bhagavad Gita translations using BERT-based language frameworkcited by 12
  22. 2021Better Neural Machine Translation by Extracting Linguistic Information from BERTcited by 5

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6 papers.

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