Naamah: A Large Scale Synthetic Sanskrit NER Corpus via DBpedia Seeding and LLM Generation

• URL: http://arxiv.org/abs/2604.26456v1
• Date: Wed, 29 Apr 2026 09:12:57 GMT
• Title: Naamah: A Large Scale Synthetic Sanskrit NER Corpus via DBpedia Seeding and LLM Generation
• Abstract summary: We introduce Naamah, a high quality silver standard Sanskrit NER dataset comprising 102,942 sentences.<n>We propose a methodology that combines entity extraction from DBpedia with the generative capabilities of a 24B parameter hybrid reasoning model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The digitisation of classical Sanskrit literature is impeded by a scarcity of annotated resources, particularly for Named Entity Recognition. While recent methodologies utilise generic Large Language Models (LLMs) for data augmentation, these approaches remain prone to error and often lack the reasoning depth required for classical grammar. In this work, we introduce Naamah, a high quality silver standard Sanskrit NER dataset comprising 102,942 sentences. We propose a methodology that combines entity extraction from DBpedia with the generative capabilities of a 24B parameter hybrid reasoning model to create grammatically natural and synthetically diverse training data. We utilize this dataset to benchmark two transformer architectures: the massive multilingual XLM RoBERTa and the parameter efficient IndicBERTv2.

Related papers

• Data Synthesis and Parameter-Efficient Fine-Tuning for Low-Resource NMT: A Case Study on Q'eqchi' Mayan [42.654087108357594]
  This study introduces a data synthesis methodology to bootstrap NMT models without scraping target-language parallel text.<n>We transform community-sourced dictionaries into a massive synthetic corpus, utilizing Efficient Fine-Tuning (PEFT) via LoRA adapters on an mT5-base model.<n> evaluation against an organic glossary reveals a structural-semantic gap (BLEU 0.59), where the model maintains grammatical integrity but lacks the lexical grounding of natural language.
  arXiv  Detail & Related papers  (2026-06-08T17:29:08Z)
• Beyond Subtokens: A Rich Character Embedding for Low-resource and Morphologically Complex Languages [5.338837380875301]
  Tokenization and sub-tokenization based models like word2vec, BERT and the GPTs are the state-of-the-art in natural language processing.<n>We propose to compute word vectors directly from character strings, integrating both semantic and syntactic information.<n>It has the potential to improve performance for both large context-based language models like BERT and small models like word2vec for under-resourced and morphologically rich languages.
  arXiv  Detail & Related papers  (2026-02-24T21:16:08Z)
• Improving Indigenous Language Machine Translation with Synthetic Data and Language-Specific Preprocessing [5.781452568978427]
  We augment curated parallel datasets for indigenous languages of the Americas with synthetic sentence pairs generated using a high-capacity multilingual translation model.<n>Experiments on Guarani-Spanish and Quechua-Spanish translation show consistent chrF++ improvements from synthetic data augmentation.<n> diagnostic experiments on Aymara highlight the limitations of generic preprocessing for highly agglutinative languages.
  arXiv  Detail & Related papers  (2026-01-06T16:06:42Z)
• Refining Sentence Embedding Model through Ranking Sentences Generation with Large Language Models [60.00178316095646]
  Sentence embedding is essential for many NLP tasks, with contrastive learning methods achieving strong performance using datasets like NLI.<n>Recent studies leverage large language models (LLMs) to generate sentence pairs, reducing annotation dependency.<n>We propose a method for controlling the generation direction of LLMs in the latent space. Unlike unconstrained generation, the controlled approach ensures meaningful semantic divergence.<n> Experiments on multiple benchmarks demonstrate that our method achieves new SOTA performance with a modest cost in ranking sentence synthesis.
  arXiv  Detail & Related papers  (2025-02-19T12:07:53Z)
• One Model is All You Need: ByT5-Sanskrit, a Unified Model for Sanskrit NLP Tasks [26.848664285007022]
  ByT5-Sanskrit is designed for NLP applications involving the morphologically rich language Sanskrit. It is easier to deploy and more robust to data not covered by external linguistic resources. We show that our approach yields new best scores for lemmatization and dependency parsing of other morphologically rich languages.
  arXiv  Detail & Related papers  (2024-09-20T22:02:26Z)
• Improving Text Embeddings with Large Language Models [59.930513259982725]
  We introduce a novel and simple method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps. We leverage proprietary LLMs to generate diverse synthetic data for hundreds of thousands of text embedding tasks across 93 languages. Experiments demonstrate that our method achieves strong performance on highly competitive text embedding benchmarks without using any labeled data.
  arXiv  Detail & Related papers  (2023-12-31T02:13:18Z)
• Beyond Contrastive Learning: A Variational Generative Model for Multilingual Retrieval [109.62363167257664]
  We propose a generative model for learning multilingual text embeddings. Our model operates on parallel data in $N$ languages. We evaluate this method on a suite of tasks including semantic similarity, bitext mining, and cross-lingual question retrieval.
  arXiv  Detail & Related papers  (2022-12-21T02:41:40Z)
• Entity-Assisted Language Models for Identifying Check-worthy Sentences [23.792877053142636]
  We propose a new uniform framework for text classification and ranking. Our framework combines the semantic analysis of the sentences, with additional entity embeddings obtained through the identified entities within the sentences. We extensively evaluate the effectiveness of our framework using two publicly available datasets from the CLEF's 2019 & 2020 CheckThat! Labs.
  arXiv  Detail & Related papers  (2022-11-19T12:03:30Z)
• The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding [65.34601470417967]
  We describe a hybrid architecture for dialogue response generation that combines the strengths of neural language modeling and rule-based generation. Our experiments show that this system outperforms both rule-based and learned approaches in human evaluations of fluency, relevance, and truthfulness.
  arXiv  Detail & Related papers  (2022-09-16T09:00:49Z)
• Learning to Synthesize Data for Semantic Parsing [57.190817162674875]
  We propose a generative model which models the composition of programs and maps a program to an utterance. Due to the simplicity of PCFG and pre-trained BART, our generative model can be efficiently learned from existing data at hand. We evaluate our method in both in-domain and out-of-domain settings of text-to-Query parsing on the standard benchmarks of GeoQuery and Spider.
  arXiv  Detail & Related papers  (2021-04-12T21:24:02Z)
• SDA: Improving Text Generation with Self Data Augmentation [88.24594090105899]
  We propose to improve the standard maximum likelihood estimation (MLE) paradigm by incorporating a self-imitation-learning phase for automatic data augmentation. Unlike most existing sentence-level augmentation strategies, our method is more general and could be easily adapted to any MLE-based training procedure.
  arXiv  Detail & Related papers  (2021-01-02T01:15:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.