Recent advances in word embedding provide significant benefit to various information processing tasks. Yet these dense representations and their estimation of word-to-word relatedness remain difficult to interpret and hard to analyze. As an alternative, explicit word representations i.e. vectors with clearly-defined dimensions, which can be words, windows of words, or documents are easily interpretable, and recent methods show competitive performance to the dense vectors. In this work, we propose a method to transfer word2vec SkipGram embedding model to its explicit representation model. The method provides interpretable explicit vectors while keeping the effectiveness of the original model, tested by evaluating the model on several word association collections. Based on the proposed explicit representation, we propose a novel method to quantify the degree of the existence of gender bias in the English language (used in Wikipedia) with regard to a set of occupations. By measuring the bias towards explicit Female and Male factors, the work demonstrates a general tendency of the majority of the occupations to male and a strong bias in a few specific occupations (e.g. nurse) to female.

本研究提出了一种采用平滑一阶共现关系来度量文本中偏见的方法，通过与向量相似性进行对比实验，对经常使用的词向量方法在量化偏见时引入非相关概念的问题进行探究，并在英文维基百科语料库上测量了职业词的性别偏见，得到了与美国职场实际情况更高相关性的结果。

使用平滑的一阶共现法测量文本语料库中的社会偏见