# Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource

arXiv: https://arxiv.org/abs/1607.00225 · HTML: https://mlsanity.com/paper/1607.00225/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2016.

## Built on by (most cited first, 7 of 7)

- [SICKNL: A Dataset for Dutch Natural Language Inference](https://mlsanity.com/paper/2101.05716/) (2021)
- [Do large language models solve verbal analogies like children do?](https://mlsanity.com/paper/2310.20384/) (2023)
- [Can Peanuts Fall in Love with Distributional Semantics?](https://mlsanity.com/paper/2301.08731/) (2023)
- [An Assessment of the Impact of OCR Noise on Language Models](https://mlsanity.com/paper/2202.00470/) (2022)
- [Classification of Geological Borehole Descriptions Using a Domain Adapted Large Language Model](https://mlsanity.com/paper/2407.10991/) (2024)
- [Traffic Event Detection as a Slot Filling Problem](https://mlsanity.com/paper/2109.06035/) (2021)
- [Reading Miscue Detection in Primary School through Automatic Speech Recognition](https://mlsanity.com/paper/2406.07060/) (2024)

## Abstract (arXiv)

Word embeddings have recently seen a strong increase in interest as a result of strong performance gains on a variety of tasks. However, most of this research also underlined the importance of benchmark datasets, and the difficulty of constructing these for a variety of language-specific tasks. Still, many of the datasets used in these tasks could prove to be fruitful linguistic resources, allowing for unique observations into language use and variability. In this paper we demonstrate the performance of multiple types of embeddings, created with both count and prediction-based architectures on a variety of corpora, in two language-specific tasks: relation evaluation, and dialect identification. For the latter, we compare unsupervised methods with a traditional, hand-crafted dictionary. With this research, we provide the embeddings themselves, the relation evaluation task benchmark for use in further research, and demonstrate how the benchmarked embeddings prove a useful unsupervised linguistic resource, effectively used in a downstream task.