Ryoma Sato, Makoto Yamada, Hisashi Kashima
We lifted 2 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| joisino/reeval-wmd | canonical | 0 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| knn_evaluation | Not yet run | joisino/reeval-wmd/util.py code served (permissive licence) · get_code("37a9a64213954c63") |
| select_k | Not yet run | joisino/reeval-wmd/util.py code served (permissive licence) · get_code("6ca4e568c0d215d4") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
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.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2105.14403")
get_code_for_paper("2105.14403")
have("2105.14403")
Connect an agent — have() is free.