Speeding up Word Mover's Distance and its variants via properties of distances between embeddings

1 Dec 2019Matheus WernerEduardo Laber

The Word Mover's Distance (WMD) proposed by Kusner et al. is a distance between documents that takes advantage of semantic relations among words that are captured by their embeddings. This distance proved to be quite effective, obtaining state-of-art error rates for classification tasks, but is also impracticable for large collections/documents due to its computational complexity... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Text Classification 20NEWS REL-RWMD k-NN Accuracy 74.78 # 10
Document Classification Amazon REL-RWMD k-NN Accuracy 93.03 # 2
Document Classification BBCSport REL-RWMD k-NN Accuracy 95.18 # 3
Document Classification Classic REL-RWMD k-NN Accuracy 96.85 # 1
Text Classification Ohsumed REL-RWMD k-NN Accuracy 58.74 # 5
Document Classification Recipe REL-RWMD k-NN Accuracy 56.80 # 2
Document Classification Reuters-21578 REL-RWMD k-NN Accuracy 95.61 # 3
Document Classification Twitter REL-RWMD k-NN Accuracy 71.05 # 2

Methods used in the Paper


METHOD TYPE
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