paper-with-me

홈 › Papers

A Word-Level Digital Reader of the Prasthanatrayi with Sankara's Bhasya: Corpus, Method, and an Open, Offline Reading Aid for the Advaita Vedanta Canon

2026-07-08 · Tamal Maharaj arxiv

The Prasthanatrayi -- the ten principal Upanisads, the Brahmasutra, and the Bhagavadgita, with Sankara's commentaries (bhasya) -- is the foundational corpus of Advaita Vedanta. Continuous euphonic combination (sandhi), long compounds (samasa), and dense scholastic prose make it hard to read at the word level: where one word ends, and what each word means grammatically, are both obscured. We present an open, fully offline, word-level digital reader of the entire Prasthanatrayi with Sankara's bhasya. Every word -- of both the root text (mula) and the commentary -- is clickable and resolves to a pop-up giving its split (padaccheda), morphological analysis, and gloss. Because every word carries a lemma, the reader also acts as a concordance: a search on a dictionary headword retrieves all of that word's inflected and sandhi-hidden occurrences, and its occurrences inside compounds, across both layers. The resource covers thirteen commentarial units (2,971 verses, sutras, and prose sections; 36,881 analysed word-occurrences of root text) and a global dictionary of 95,587 distinct commentarial surface forms. We describe the corpus, the hybrid pipeline -- a rule-based sandhi splitter over an inflected-form lexicon and attested-corpus look-ups, with LLM-assisted analysis under an adversarial two-pass verification protocol -- and a durable human-review loop whose corrections survive every regeneration. An intrinsic evaluation against independent Sanskrit resources finds high-confidence analyses agree with an authoritative inflectional lexicon on over 99% of attested forms, and a band-blind adjudication confirms that quality degrades predictably across confidence bands, with errors concentrated in the low-confidence tier the review loop targets. The reader is a single self-contained HTML file needing no server or network, offered as a freely redistributable teaching and reading aid.

📄 PDF Abstract BibTeX arXiv:2607.07282

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

How Curiosity can be modeled for a Clickbait Detector

2018-06-11 · Lasya Venneti, Aniket Alam

The impact of continually evolving digital technologies and the proliferation of communications and content has now been widely acknowledged to be central to understanding our world. What is less acknowledged is that thi…

Subword-augmented Embedding for Cloze Reading Comprehension

2018-06-24 · COLING 2018 8 · Zhuosheng Zhang, Yafang Huang, Hai Zhao

Representation learning is the foundation of machine reading comprehension. In state-of-the-art models, deep learning methods broadly use word and character level representations. However, character is not naturally the …

Machine Reading ComprehensionReading ComprehensionRepresentation Learning

Word Complexity is in the Eye of the Beholder

2021-06-01 · NAACL 2021 4 · Sian Gooding, Ekaterina Kochmar, Seid Muhie Yimam, Chris Biemann

Lexical complexity is a highly subjective notion, yet this factor is often neglected in lexical simplification and readability systems which use a {''}one-size-fits-all{''} approach. In this paper, we investigate which a…

Lexical Simplification

The Lipreading Gap: Do VSR Models Perceive Visual Speech Like Human Lipreaders?

2026-06-05 · Rishabh Jain, Naomi Harte arxiv

Visual speech recognition (VSR) models now surpass human lipreaders on benchmarks, but do such gains establish human-like visual speech perception? To explore this, we compare three VSR systems with human baselines on th…

Visual Speech Recognition

Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding

2022-12-19 · Haoli Bai, Zhiguang Liu, Xiaojun Meng, Wentao Li 외

Unsupervised pre-training on millions of digital-born or scanned documents has shown promising advances in visual document understanding~(VDU). While various vision-language pre-training objectives are studied in existin…

Contrastive Learningdocument understandingOptical Character Recognition (OCR)Unsupervised Pre-training