Short Text Clustering on Biomedical (ACC, NMI)
0.548AccuracySelf-Train
Evaluation Results
| Method | Links | ||
|---|---|---|---|
| Self-TrainAlgorithm=SIF + Autoencoder2021.03 | 0.548 | 0.471 | |
| RSTC2023.05 | 0.484 | 0.4012 | |
| RSTC-Ccontrastive learning=class-wise, pseudo-labels=SAOT2023.05 | 0.4674 | 0.3869 | |
| SCCLAveraging=Average over five random runs2021.03 | 0.462 | 0.415 | |
| STCCAlgorithm=Word2Vec + CNN + K-means2021.03 | 0.436 | 0.381 | |
| STC2-LPI2023.05 | 0.4337 | 0.3802 | |
| SCCL2023.05 | 0.4249 | 0.3916 | |
| K-means_IC2023.05 | 0.4044 | 0.3216 | |
| HAC-SDAlgorithm=Hierarchical Agglomerative Clustering2021.03 | 0.401 | 0.335 | |
| Self-Train2023.05 | 0.4006 | 0.3446 | |
| SBERT(k-means)2023.05 | 0.395 | 0.3263 | |
| RSTC-OTpseudo-labels=traditional OT2023.05 | 0.3814 | 0.3489 | |
| RSTC-Icontrastive learning=instance-wise, clustering=k-means2023.05 | 0.3439 | 0.312 | |
| TF-IDF2023.05 | 0.2913 | 0.2512 | |
| TF-IDFFeature dimension=1500, Algorithm=K-means2021.03 | 0.283 | 0.232 | |
| BoWFeature dimension=1500, Algorithm=K-means2021.03 | 0.143 | 0.092 | |
| BOW2023.05 | 0.1418 | 0.0851 |