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.

我们提出 AsNER，这是一个适用于资源匮乏的阿萨姆语的命名实体注释数据集，其中包括来自印度总理演讲和阿萨姆语剧本的约99k个单词，数据集中包含了人名，地名和地址信息，并基于 Fasttext, BERT, XLM-R, FLAIR, MuRIL等算法进行评估。其中最高的 F1-score 在使用MuRIL作为词嵌入方法时达到了80.69％的准确率。

AsNER——用于阿萨姆语命名实体识别的注释数据集和基准线