Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource
This work provides a benchmark for Dutch NLP tasks, but it is incremental as it applies existing methods to a new language context.
The paper evaluated unsupervised Dutch word embeddings on relation evaluation and dialect identification tasks, showing that benchmarked embeddings can serve as a useful linguistic resource for downstream applications.
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.