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Moving Other Way: Exploring Word Mover Distance Extensions

arXiv 2202.03119 ↗First posted 7 Feb 2022cs.CLcited by 0

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Abstract (arXiv)

The word mover's distance (WMD) is a popular semantic similarity metric for two texts. This position paper studies several possible extensions of WMD. We experiment with the frequency of words in the corpus as a weighting factor and the geometry of the word vector space. We validate possible extensions of WMD on six document classification datasets. Some proposed extensions show better results in terms of the k-nearest neighbor classification error than WMD.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 27 references that are in the map. First posted on arXiv 7 Feb 2022.

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References · 16

  1. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
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  4. 2017Poincar'e Embeddings for Learning Hierarchical Representationscited by 674
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  9. 2018Discrete Adversarial Attacks and Submodular Optimization with Applications to Text Classificationcited by 34
  10. 2020Word Rotator's Distancecited by 32
  11. 2020Style-transfer and Paraphrase: Looking for a Sensible Semantic Similarity Metriccited by 23
  12. 2021Re-evaluating Word Mover's Distancecited by 10
4 more
  1. 2018How much does a word weigh? Weighting word embeddings for word sense inductioncited by 6
  2. 2020Improving Text Generation Evaluation with Batch Centering and Tempered Word Mover Distancecited by 5
  3. 2019Natural Alpha Embeddingscited by 4
  4. 2021Rethinking Crowd Sourcing for Semantic Similaritycited by 2

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