ABSTRACT We introduce a word embedding method that generates a set of real-valued word vectors from a distributional semantic space. The semantic space is built with a set of context units (words) which are selected by an entropy-based feature selection approach with respect to the certainty involved in their contextual environments. We show that the most predictive context of a target word is its preceding word. An adaptive transformation function is also introduced that reshapes the data distribution to make it suitable for dimensionality reduction techniques. The final low-dimensional word vectors are formed by the singular vectors of a matrix of transformed data. We show that the resulting word vectors are as good as other sets of word vectors generated with popular word embedding methods.

中文翻译：

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实值句法词向量

摘要 我们介绍了一种词嵌入方法，该方法从分布语义空间生成一组实值词向量。语义空间是用一组上下文单元（词）构建的，这些单元是通过基于熵的特征选择方法根据上下文环境中涉及的确定性来选择的。我们表明目标词的最具预测性的上下文是它的前一个词。还引入了自适应变换函数，该函数可重塑数据分布，使其适用于降维技术。最终的低维词向量由变换数据矩阵的奇异向量构成。我们表明，生成的词向量与使用流行的词嵌入方法生成的其他词向量集一样好。