Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource

07/01/2016 ∙ by Stéphan Tulkens, et al. ∙ 0

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.



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Code Repositories


Repository for the word embeddings experiments described in "Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource", presented at LREC 2016.

view repo
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