Natural Language Processing (NLP)
Enterprise AI Analysis for Naamah: A Large Scale Synthetic Sanskrit NER Corpus via DBpedia Seeding and LLM Generation
This paper introduces Naamah, a novel, large-scale synthetic Sanskrit Named Entity Recognition (NER) corpus of 102,942 sentences. It addresses the scarcity of annotated Sanskrit resources by combining entity extraction from DBpedia with a 24-billion-parameter hybrid reasoning LLM (Sarvam-M) for generation. The methodology bypasses rigid grammar templates, yielding morphologically diverse and syntactically natural data. Benchmarking shows IndicBERTv2 outperforms XLM-ROBERTa, highlighting the importance of domain-adapted tokenization over raw model scale for low-resource classical languages.
Executive Impact: Key Performance Metrics
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Total Sentences
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Validation F1 (IndicBERTv2)
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Unique Tokens
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The core innovation is the hybrid data generation pipeline, combining DBpedia entity seeding with a 24B-parameter Indic-optimized LLM (Sarvam-M). This ensures grammatical naturalness and semantic diversity, overcoming the limitations of template-based generation and cross-lingual projection.
Benchmarking on Naamah revealed that IndicBERTv2 (0.9615 F1) outperforms XLM-ROBERTa (0.9506 F1). This indicates that domain-aligned tokenization, which preserves Sanskrit morphological cues, is more critical than sheer model scale for classical low-resource languages.
Qualitative analysis showed XLM-ROBERTa's generic tokenizer fracturing agglutinated Sanskrit terms (e.g., 'Kuruksetre' into 'Kuruksetra' and 'e'), leading to misclassification. IndicBERTv2's domain-adapted tokenizer, however, maintained semantic integrity, correctly classifying complete entity spans.
0.9615
IndicBERTv2 Validation F1 Score
Naamah Data Generation Flow
DBpedia Entity Mining
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LLM Sentence Generation (Sarvam-M)
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Heuristic Preprocessing & Filtering
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102,942 Silver Standard Sentences
| Metric | XLM-ROBERTa | IndicBERTv2 |
|---|---|---|
| F1 Score | 0.950581 | 0.961451 |
| Precision | 0.949766 | 0.959563 |
| Recall | 0.951396 | 0.963345 |
| Model Size | Large | Compact (130MB) |
| Tokenization | Generic Multilingual | Domain Adapted (Indic) |
Impact of Domain-Adapted Tokenization
The study revealed a critical failure mode for multilingual models like XLM-ROBERTa on Sanskrit's agglutinative nature. For the term 'Kuruksetre' (in Kurukshetra), XLM-ROBERTa fractured it into 'Kuruksetra' (Location) and 'e' (incorrectly classified as Organization).
In contrast, IndicBERTv2, with its Indic-oriented tokenizer, correctly identified 'Kuruksetre' as a single Location entity. This highlights that for morphologically complex, low-resource languages, a specialized tokenizer is more effective than raw parameter count in preserving semantic integrity and improving NER accuracy.
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