Evaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource

Stéphan Tulkens*, Chris Emmery, Walter Daelemans

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    27 Citations (Scopus)


    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.
    Original languageEnglish
    Title of host publicationEvaluating Unsupervised Dutch Word Embeddings as a Linguistic Resource
    PublisherAssociation for Computational Linguistics
    Publication statusPublished - 1 Jul 2016
    EventInternational Conference on Language Resources and Evaluation 2016: 10th edition - Grand Hotel Bernardin Conference Center, Portoroz, Slovenia
    Duration: 23 May 201628 May 2016
    Conference number: 10


    ConferenceInternational Conference on Language Resources and Evaluation 2016
    Abbreviated titleLREC 2016
    Internet address


    • Word Embeddings
    • Evaluation
    • Dutch


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