Named entity recognition (NER), particularly fine-grained NER (FgNER), extracts domain-specific entity information for Natural Language Processing (NLP) applications such as knowledge base construction and relation extraction. While manual annotation for creating relevant data is expensive, distant supervision often produces noisy data. Moreover, resources for coarse-grained and fine-grained NER in Indian languages, particularly in the vulnerable languages of India’s North Eastern Region, remain scarce. This work aims at creating such a resource for three vulnerable languages: <i>Bodo/Boro (brx)</i>, <i>Manipuri/Meitei (mni)</i>, and <i>Mizo/Lushai (lus)</i>, which are regarded as official languages in three Indian states and spoken by more than six million people across five countries in South and Southeast Asia. We use annotations projection from high-resource FgNER datasets using source-to-target parallel corpora and a projection tool built on a multilingual encoder. The dataset comprises over 198k sentences, 282k entities, and 2.8M tokens in each low-resource language. Our thorough analyses validate the dataset’s high quality. We further explore zero-shot and cross-lingual settings, examining the impact of script similarity and multilingualism in cross-lingual FgNER performance. The dataset, expert detector models, the agentic tool, and the interactive web application are available as open-source resources at: https://hf.co/collections/prachuryyaIITG/finerviner.

We introduce CLASSER, a cross-lingual annotation projection framework enhanced through script similarity, to create fine-grained named entity recognition (FgNER) datasets for low-resource languages. Manual annotation for named entity recognition (NER) is expensive, and distant supervision often produces noisy data that are often limited to high-resource languages. CLASSER employs a two-stage process: first projection of annotations from high-resource NER datasets to target language by using source-to-target parallel corpora and a projection tool built on a multilingual encoder, then refining them by leveraging datasets in script-similar languages. We apply this to five low-resource Indian languages: Assamese, Marathi, Nepali, Sanskrit, and Bodo, a vulnerable language. The resulting dataset comprises 1.8M sentences, 2.6M entity mentions and 24.7M tokens. Through rigorous analyses, the effectiveness of our method and the high quality of the resulting dataset are ascertained with F1 score improvements of 26% in Marathi and 46% in Sanskrit over the current state-of-the-art. We further extend our analyses to zero-shot and cross-lingual settings, systematically investigating the impact of script similarity and multilingualism on cross-lingual FgNER performance. The dataset is publicly available at huggingface.co/datasets/prachuryyaIITG/CLASSER.