Word similarity calculation on the dataset Mturk-771, MEN, RW-STANFORD, SimLex-999, SimVerb-3500.
- path_similarity, wup_similarity, lch_similarity, res_similarity, jcn_similarity, lin_similarity (WordNet-based)
- WebJaccard, WebOverlap, WebDice, WebPMI, NGD (Google Search Based)
- LSA-Wikipedia, LDA-Wikipedia (Wikipedia-based)
- Word2Vec, Fasttext, GloVe, ELMo, BERT (Representation Learning)
| Method | MTurk-771 (spearmanr) | MEN (spearmanr) | RW-STANFORD(spearmanr) | SimLex-999(spearmanr) | SimVerb-3500(spearmanr) |
|---|---|---|---|---|---|
| Path | 0.4985 | 0.3342 | -0.0003 | 0.4370 | 0.4538 |
| Wup | 0.4550 | 0.3589 | 0.0252 | 0.4137 | 0.4080 |
| Lch | 0.4960 | 0.3544 | 0.0086 | 0.4097 | 0.4493 |
| Resnik | 0.4168 | 0.3610 | 0.0539 | 0.3595 | 0.4471 |
| Jcn | 0.4823 | 0.3343 | 0.0019 | 0.4574 | 0.4629 |
| Lin | 0.4931 | 0.3338 | 0.0147 | 0.4047 | 0.4712 |
| WebJaccard | 0.3272 | 0.4516 | 0.1503 | 0.0871 | 0.0021 |
| WebOverlap | 0.2346 | 0.3953 | 0.0416 | 0.0778 | 0.0235 |
| WebDice | 0.3351 | 0.4365 | 0.1610 | 0.0871 | 0.0010 |
| WebPMI | 0.3272 | 0.4316 | 0.1503 | 0.0871 | 0.0021 |
| NGD | 0.3282 | 0.4671 | -0.2297 | 0.1592 | -0.0446 |
| LSA-Wikipedia | 0.5851 | 0.4663 | 0.1838 | 0.2094 | 0.1038 |
| LDA-Wikipedia | 0.1936 | 0.2815 | 0.0265 | 0.0164 | 0.0165 |
| Word2Vec | 0.6713 | 0.7321 | 0.4527 | 0.4420 | 0.3635 |
| FastText | 0.7529 | 0.8362 | 0.5713 | 0.4644 | 0.3649 |
| GloVe | 0.7152 | 0.8016 | 0.4512 | 0.4083 | 0.2832 |
| ELMo | 0.6938 | 0.4698 | 0.2570 | 0.3151 | 0.2407 |
| BERT(Embedding) | 0.0019 | 0.0668 | 0.2021 | 0.0801 | 0.0487 |