Padārtha: Ontology-Grounded Fine-Grained NER Benchmark for Classical Sanskrit

2026-08-29

AI Summary

研究针对梵文经典文献开发了基于印度本体论系统的细粒度命名实体识别基准Padārtha，通过专家标注和系统评测解决了现代标签集不适用的问题。

Abstract

Annotation schemas are not neutral. When applied to classical literature, tag sets developed for modern journalistic texts impose source-culture definitions on texts they were never designed to describe. We instead ground a schema in the tradition of the text itself introducing \textit{Padārtha}, the first ontology-grounded fine-grained Named Entity Recognition (NER) benchmark for Sanskrit, built on the \textit{Mahābhārata} epic. Our tag set derives from \textit{Nyāya-Vaiśesika}, a classical Indian ontological system, yielding 18 fine-grained categories organized under 10 ontological nodes and mapped onto five standard coarse tags, ensuring interoperability with existing benchmarks. Expert annotators label over 12.6K entries from a scholarly index of named entities, linked to corresponding mentions in the \textit{Mahānāma} corpus, producing fine-grained annotations for 108,335 entity mentions across 73,632 verses, along with a 5,000-verse expert-verified test set sampled to stress rare mentions. We present the first systematic benchmarking of generative NER against traditional architectures for Sanskrit, finding that fine-tuned generative models perform comparably to task-specific systems. However, all systems show a sharp decline from coarse to fine granularity and struggle with out-of-entity mentions unseen during training. The limitation is not due to data scarcity alone, as fine-tuned models recall unseen entities far worse than seen ones and tend to default to the majority sense under lexical ambiguity.

Problem

• Ontology-Grounded
• Fine-Grained NER
• Classical Sanskrit
• Annotation Schema
• Nyāya-Vaiśeṣika

Innovation

• ontology-grounded
• fine-grained NER
• Sanskrit
• Nyāya-Vaiśeṣika