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In this paper, our main contributions are that embeddings from relativelysmaller corpora can outperform ones from larger corpora and we make the newSwedish analogy test set publicly available. To achieve a good networkperformance in natural language processing (NLP) downstream tasks, severalfactors play important roles: dataset size, the right hyper-parameters, andwell-trained embeddings. We show that, with the right set of hyper-parameters,good network performance can be reached even on smaller datasets. We evaluatethe embeddings at both the intrinsic and extrinsic levels. The embeddings aredeployed with the Transformer in named entity recognition (NER) task andsignificance tests conducted. This is done for both Swedish and English. Weobtain better performance in both languages on the downstream task with smallertraining data, compared to recently released, Common Crawl versions; andcharacter n-grams appear useful for Swedish, a morphologically rich language.

Exploring Swedish & English fastText Embeddings for NER with the Transformer