Using word embeddings for immigrant and refugee stereotype quantification in a diachronic and multilingual setting
Danielly Sorato,
Martin Lundsteen,
Carme Colominas Ventura
et al.
Abstract:Word embeddings are efficient machine-learning-based representations of human language used in many Natural Language Processing tasks nowadays. Due to their ability to learn underlying word association patterns present in large volumes of data, it is possible to observe various sociolinguistic phenomena in the embedding semantic space, such as social stereotypes. The use of stereotypical framing in discourse can be detrimental and induce misconceptions about certain groups, such as immigrants and refugees, esp… Show more
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