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Found 8 papers, 3 papers with code
The Undesirable Dependence on Frequency of Gender Bias Metrics Based on Word Embeddings
In this work we study the effect of frequency when measuring female vs. male gender bias with word embedding-based bias quantification methods.
Investigating the Frequency Distortion of Word Embeddings and Its Impact on Bias Metrics
In the present work, we systematically study the association between frequency and semantic similarity in several static word embeddings.
On the Interpretability and Significance of Bias Metrics in Texts: a PMI-based Approach
In recent years, word embeddings have been widely used to measure biases in texts.
Gender bias in magazines oriented to men and women: a computational approach
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.
Zero-shot Multi-Domain Dialog State Tracking Using Descriptive Rules
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.
Using contextual information for automatic triage of posts in a peer-support forum
Mental health forums are online spaces where people can share their experiences anonymously and get peer support.
Corpus specificity in LSA and Word2vec: the role of out-of-domain documents
In the present article we investigate whether LSA and Word2vec capacity to identify relevant semantic dimensions increases with size of corpus.
Comparative study of LSA vs Word2vec embeddings in small corpora: a case study in dreams database
Word embeddings have been extensively studied in large text datasets.
