  The word mover's distance (WMD) is a fundamental technique for measuring the
similarity of two documents. As the crux of WMD, it can take advantage of the
underlying geometry of the word space by employing an optimal transport
formulation. The original study on WMD reported that WMD outperforms classical
baselines such as bag-of-words (BOW) and TF-IDF by significant margins in
various datasets. In this paper, we point out that the evaluation in the
original study could be misleading. We re-evaluate the performances of WMD and
the classical baselines and find that the classical baselines are competitive
with WMD if we employ an appropriate preprocessing, i.e., L1 normalization. In
addition, we introduce an analogy between WMD and L1-normalized BOW and find
that not only the performance of WMD but also the distance values resemble
those of BOW in high dimensional spaces.
