A pipeline that translates sentences and entities, then matches entities by orthographic, phonetic, and distributional similarity, improves cross-lingual NER over prior projection baselines.
pioNER: Datasets and Baselines for Armenian Named Entity Recognition
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abstract
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.
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Entity Projection via Machine Translation for Cross-Lingual NER
A pipeline that translates sentences and entities, then matches entities by orthographic, phonetic, and distributional similarity, improves cross-lingual NER over prior projection baselines.