AsNER -- Annotated Dataset and Baseline for Assamese Named Entity recognition

TL;DR

This paper introduces AsNER, a new annotated dataset for Assamese NER, along with baseline models demonstrating promising results, which can significantly advance NLP research for the low-resource Assamese language.

Contribution

The paper provides the first large-scale Assamese NER dataset and benchmarks multiple state-of-the-art models, establishing a foundation for future research in low-resource language processing.

Findings

MuRIL-based model achieved 80.69% F1-score.

The dataset includes 99k tokens from diverse sources.

Baseline models demonstrate the dataset's utility for Assamese NER.

Abstract

We present the AsNER, a named entity annotation dataset for low resource Assamese language with a baseline Assamese NER model. The dataset contains about 99k tokens comprised of text from the speech of the Prime Minister of India and Assamese play. It also contains person names, location names and addresses. The proposed NER dataset is likely to be a significant resource for deep neural based Assamese language processing. We benchmark the dataset by training NER models and evaluating using state-of-the-art architectures for supervised named entity recognition (NER) such as Fasttext, BERT, XLM-R, FLAIR, MuRIL etc. We implement several baseline approaches with state-of-the-art sequence tagging Bi-LSTM-CRF architecture. The highest F1-score among all baselines achieves an accuracy of 80.69% when using MuRIL as a word embedding method. The annotated dataset and the top performing model are made publicly available.