In recent years, the use of word embeddings has become popular to measure the presence of biases in texts.
Besides, we show that in 2012, the content associated with horoscope increased in the women-oriented magazine, generating a new gap that remained open over time.
In this work, we present a framework for incorporating descriptive logical rules in state-of-the-art neural networks, enabling them to learn how to handle unseen labels without the introduction of any new training data.
Mental health forums are online spaces where people can share their experiences anonymously and get peer support.
In the present article we investigate whether LSA and Word2vec capacity to identify relevant semantic dimensions increases with size of corpus.
Word embeddings have been extensively studied in large text datasets.