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pioNER: Datasets and Baselines for Armenian Named Entity Recognition

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arxiv 1810.08699 v1 pith:FBRJIQYG submitted 2018-10-19 cs.CL

Tsolak Ghukasyan , Garnik Davtyan , Karen Avetisyan , Ivan Andrianov This is my paper · ORCID
classification cs.CL
keywords namedentityarmeniandatasetsrecognitioncorpusmodelsnews
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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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  1. Entity Projection via Machine Translation for Cross-Lingual NER

    cs.CL 2019-08 conditional novelty 6.0 of 10

    A pipeline that translates sentences and entities, then matches entities by orthographic, phonetic, and distributional similarity, improves cross-lingual NER over prior projection baselines.

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