Word Embedding Association Test (Caliskan et al. 2017) Normalized Association Score (Caliskan et al. 2017) 2019)Įmbedding Coherence Test (Dev & Phillips. Relative Negative Sentiment Bias (Sweeney & Najafian. Relative Norm Distance (Garg et al. 2018) Mean Average Cosine Similarity (Mazini et al. 2019) Query and calculate_es() to calculate the effect size. It is recommended to use the function query() to make a Qualify as attribute words because we know they are related to a certain “brother”, “he”, “him”, “his”, “son” to study gender bias. For example, Caliskan etĪl. (2017) used gender-related words such as “male”, “man”, “boy”, Relation to the bias that one is studying. Please note that also T_words is not always required. “dance”, “literature”, “novel”, “symphony”, “drama”, “sculpture”). “computation”, “numbers”, “addition”) and arts (“poetry”, “art”, Mathematics (“math”, “algebra”, “geometry”, “calculus”, “equations”, ForĮxample, Caliskan et al. (2017) grouped target words into two groups: T_words, to group words by their perceived bias. Separate these words into two sets, S_words and Target words to study the gender bias in word embeddings. Instance, the words such as “nurse” and “professor” can be used as In the case of studyingīiases, these are words that should have no bias. This package uses the “STAB” notation from Brunet et alĪll tests depend on two types of words. Install.packages( "sweater") Notation of a queryĪll tests in this package use the concept of queries (see Badilla etĪl., 2020) to study associations in the input word embeddings Recommended: install the latest development version Journal of Open Source Software, 7(72), 4036, Ĭitation(package = "sweater"). sweater: Speedy Word Embedding Association Test andĮxtras Using R. To reproduce the analysis in Mazini et al (2019),Ĭhan, C., (2022). Reproduce the analysis in Garg et al (2018), please consider using the Java program or the R package cbn by Lowe. If your goal is to reproduce the analysis in Caliskan et al (2017), This package provides extra methods such as Relative Norm Distance,Įmbedding Coherence Test, SemAxis and Relative Negative Sentiment Original implementation proposed by Caliskan et al (2017). Implemented in C++, or are speedy but accurate approximation of the The package provides functions that are speedy. To test for unwanted associations, or biases. The methods provided by this package can also be used R) is to test for associations among words in wordĮmbedding spaces.
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