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• W3172230358 abstract "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." @default.
• W3172230358 created "2021-06-22" @default.
• W3172230358 creator A5031707680 @default.
• W3172230358 creator A5040188906 @default.
• W3172230358 creator A5046358579 @default.
• W3172230358 date "2021-05-29" @default.
• W3172230358 modified "2023-10-16" @default.
• W3172230358 title "Re-evaluating Word Mover's Distance" @default.
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• W3172230358 doi "https://doi.org/10.48550/arxiv.2105.14403" @default.
• W3172230358 hasPublicationYear "2021" @default.
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