In this work, we tackle the problem of Armenian named entity recognition, providing silver- and gold-standard datasets as well as establishing baseline results on popular models. We present a 163000-token named entity corpus automatically generated and annotated from Wikipedia, and another 53400-token corpus of news sentences with manual annotation of people, organization and location named entities. The corpora were used to train and evaluate several popular named entity recognition models. Alongside the datasets, we release 50-, 100-, 200-, 300-dimensional GloVe word embeddings trained on a collection of Armenian texts from Wikipedia, news, blogs, and encyclopedia.
@article{arxiv.1810.08699,
title = {pioNER: Datasets and Baselines for Armenian Named Entity Recognition},
author = {Tsolak Ghukasyan and Garnik Davtyan and Karen Avetisyan and Ivan Andrianov},
journal= {arXiv preprint arXiv:1810.08699},
year = {2020}
}
Comments
Accepted paper at Ivannikov ISP RAS Open Conference 2018. \c{opyright} 2018 IEEE