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  • 1.
    Jonasson, Michael
    Linköping University, Department of Computer and Information Science.
    Fördomsfulla associationer i en svenskvektorbaserad semantisk modell2019Independent thesis Basic level (degree of Bachelor), 12 HE creditsStudent thesis
    Abstract [en]

    Word embeddings are a powerful technique where word meaning can be represented by vectors containing actual numbers. The vectors allow  geometric operations that capture semantically important relationships between the words. In this study WEAT is applied in order to examine whether statistical properties of words pertaining to bias can be found in a swedish word embedding trained on a corpus from a swedish newspaper. The results shows that the word embedding can represent several of the IAT documented biases that where tested. A second method, WEFAT, is applied to the word embedding in order to explore the embeddings ability to represent actual statistical properties, which is also done successfully. The results from this study lends support to the validity of both methods aswell as illuminating the issue of problematic relationships between words in word embeddings.

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  • harvard1
  • ieee
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  • en-GB
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