Table 6.
Results of the grammar-based and competitive methods on the Microsoft Research Paraphrase Corpus.
| Category | Metric | Accuracy | Precision | Recall | F-measure |
|---|---|---|---|---|---|
| Corpus-based | PMI-IR | 69.90 | 70.20 | 95.20 | 81.00 |
| LSA | 68.40 | 69.70 | 95.20 | 80.50 | |
| STS Meth. | 72.64 | 74.65 | 89.13 | 81.25 | |
| SyMSS_JCN | 70.87 | 74.70 | 84.17 | 79.02 | |
| SyMSS_Vector | 70.82 | 74.15 | 90.32 | 81.44 | |
| Omiotis | 69.97 | 70.78 | 93.40 | 80.52 | |
|
| |||||
| Lexicon-based | JC | 69.30 | 72.20 | 87.10 | 79.00 |
| LC | 69.50 | 72.40 | 87.00 | 79.00 | |
| Lesk | 69.30 | 72.40 | 86.60 | 78.90 | |
| L | 69.30 | 71.60 | 88.70 | 79.20 | |
| W&P | 69.00 | 70.20 | 92.10 | 80.00 | |
| R | 69.00 | 69.00 | 96.40 | 80.40 | |
| M | 70.30 | 69.60 | 97.70 | 81.30 | |
|
| |||||
| Machine learning-based | Wan et al. [58] | 75.00 | 77.00 | 90.00 | 83.00 |
| Z&P | 71.90 | 74.30 | 88.20 | 80.70 | |
| Qiu et al. [59] | 72.00 | 72.50 | 93.40 | 81.60 | |
|
| |||||
| Baselines | Random | 51.30 | 68.30 | 50.00 | 57.80 |
| VSM | 65.40 | 71.60 | 79.50 | 75.30 | |
|
| |||||
| LG | 71.02 | 73.90 | 91.07 | 81.59 | |