pioNER: Datasets and Baselines for Armenian Named Entity Recognition
This work addresses the lack of resources for Armenian NER, providing datasets and benchmarks for the NLP community, though it is incremental as it applies existing methods to new data.
The authors tackled the problem of Armenian named entity recognition by creating silver- and gold-standard datasets, including a 163,000-token corpus from Wikipedia and a 53,400-token news corpus, and established baseline results on popular models, achieving competitive performance.
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.