  Word embeddings represent words as multidimensional real vectors,
facilitating data analysis and processing, but are often challenging to
interpret. Independent Component Analysis (ICA) creates clearer semantic axes
by identifying independent key features. Previous research has shown ICA's
potential to reveal universal semantic axes across languages. However, it
lacked verification of the consistency of independent components within and
across languages. We investigated the consistency of semantic axes in two ways:
both within a single language and across multiple languages. We first probed
into intra-language consistency, focusing on the reproducibility of axes by
performing ICA multiple times and clustering the outcomes. Then, we
statistically examined inter-language consistency by verifying those axes'
correspondences using statistical tests. We newly applied statistical methods
to establish a robust framework that ensures the reliability and universality
of semantic axes.
