all AI news
Re-evaluating Word Mover's Distance. (arXiv:2105.14403v3 [cs.LG] UPDATED)
Web: http://arxiv.org/abs/2105.14403
June 16, 2022, 1:11 a.m. | Ryoma Sato, Makoto Yamada, Hisashi Kashima
cs.LG updates on arXiv.org arxiv.org
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 …
More from arxiv.org / cs.LG updates on arXiv.org
Latest AI/ML/Big Data Jobs
Machine Learning Researcher - Saalfeld Lab
@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia
Project Director, Machine Learning in US Health
@ ideas42.org | Remote, US
Data Science Intern
@ NannyML | Remote
Machine Learning Engineer NLP/Speech
@ Play.ht | Remote
Research Scientist, 3D Reconstruction
@ Yembo | Remote, US
Clinical Assistant or Associate Professor of Management Science and Systems
@ University at Buffalo | Buffalo, NY