Axis Tour: Word Tour Determines the Order of Axes in ICA-transformed Embeddings

Word embedding is one of the most important components in natural language processing, but interpreting high-dimensional embeddings remains a challenging problem. To address this problem, Independent Component Analysis (ICA) is identified as an effective solution. ICA-transformed word embeddings reveal interpretable semantic axes; however, the order of these axes are arbitrary. In this study, we focus on this property and propose a novel method, Axis Tour, which optimizes the order of the axes. Inspired by Word Tour, a onedimensional word embedding method, we aim to improve the clarity of the word embedding space by maximizing the semantic continuity of the axes. Furthermore, we show through experiments Figure 1: Scatterplots of normalized ICA-transformed on downstream tasks that Axis Tour word embeddings whose axes are ordered by Axis Tour constructs better low-dimensional embeddings and Skewness Sort. In the upper part, Axis Tour is applied compared to both PCA and ICA.