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The research team launched the Naamah dataset, generating 103,000 high-quality Sanskrit NER sentences via DBpedia entity extraction and a 24B-parameter hybrid reasoning model, while comparing the performance of XLM-RoBERTa and IndicBERTv2.
Section 01
The research team launched the Naamah dataset, generating 103,000 high-quality Sanskrit NER sentences via DBpedia entity extraction and a 24B-parameter hybrid reasoning model, while comparing the performance of XLM-RoBERTa and IndicBERTv2. This dataset is currently the largest synthetic Sanskrit NER dataset, providing an innovative path for the digitization of low-resource classical languages.
Section 02
The digitization of classical Sanskrit literature has long been limited by the lack of high-quality NER annotated corpora. As the core carrier of Indian classical academic, religious, and philosophical literature, Sanskrit digitization is of great significance for humanities research and cross-lingual knowledge graph construction. However, the cost of traditional manual annotation is extremely high, and the insufficiency of general large language models in classical grammar reasoning results in poor quality of automatic annotation.
Section 03
Extract Sanskrit-related entities from the DBpedia knowledge base as seeds, using its cross-lingual entity alignment information to provide a reliable starting point.
Adopting a 24B-parameter hybrid reasoning model, which has three key advantages:
Section 04
Use the Naamah dataset to train two Transformer architectures to verify the dataset's quality:
As a benchmark model for cross-lingual transfer, it is pre-trained on 100 languages and can fully leverage the advantages of cross-lingual knowledge transfer.
Focused on the Indian language family, it features a parameter-efficient design and achieves or even surpasses the performance of general multilingual models with fewer parameters within the specific language family.
Section 05
The Naamah dataset provides an important reference for low-resource classical language NLP research. Its methodology of 'knowledge base seeds + domain-specific large model generation' can be extended to the processing of other classical languages such as Pali and Tibetan. Meanwhile, the development of hybrid reasoning architectures demonstrates the potential of large models in deep understanding tasks for low-resource languages.
Section 06
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