# Corpora Compared: The Case of the Swedish Gigaword & Wikipedia Corpora

arXiv: https://arxiv.org/abs/2011.03281 · HTML: https://mlsanity.com/paper/2011.03281/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Dynaword: From One-shot to Continuously Developed Datasets](https://mlsanity.com/paper/2508.02271/) (2025)
- [Bipol: Multi-axes Evaluation of Bias with Explainability in Benchmark Datasets](https://mlsanity.com/paper/2301.12139/) (2023)
- [SuperSim: a test set for word similarity and relatedness in Swedish](https://mlsanity.com/paper/2104.05228/) (2021)

## Abstract (arXiv)

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.