  In this work, we show that the difference in performance of embeddings from
differently sourced data for a given language can be due to other factors
besides data size. Natural language processing (NLP) tasks usually perform
better with embeddings from bigger corpora. However, broadness of covered
domain and noise can play important roles. We evaluate embeddings based on two
Swedish corpora: The Gigaword and Wikipedia, in analogy (intrinsic) tests and
discover that the embeddings from the Wikipedia corpus generally outperform
those from the Gigaword corpus, which is a bigger corpus. Downstream tests will
be required to have a definite evaluation.
