Table 9.
Performance with different word embedding and different position embedding size
| Word embedding | Position embedding | P | R | F1 |
|---|---|---|---|---|
| random | 0 | 62.08% | 58.77% | 60.38% |
| 5 | 69.34% | 55.9% | 61.9% | |
| 10 | 70.76% | 54.36% | 61.48% | |
| Wiki_bow_8w_25n | 0 | 60.89% | 54.46% | 57.5% |
| 5 | 59.2% | 60.72% | 59.95% | |
| 10 | 70.64% | 53.54% | 60.91% | |
| Bio_skip_8w_25n | 0 | 62.39% | 57.85% | 60.03% |
| 5 | 67.8% | 53.33% | 59.7% | |
| 10 | 66.92% | 55.18% | 60.48% | |
| Bio_skip_10w_10n | 0 | 70.66% | 49.64% | 58.31% |
| 5 | 61.84% | 56.51% | 59.06% | |
| 10 | 68.77% | 54.87% | 61.04% | |
| Bio_bow_8w_25n | 0 | 64.09% | 54.36% | 58.82% |
| 5 | 69.43% | 54.05% | 60.78% | |
| 10 | 67.27% | 49.95% | 57.33% | |
| Bio_bow_5w_10n | 0 | 58.25% | 59.38% | 58.81% |
| 5 | 60.18% | 61.23% | 60.7% | |
| 10 | 65.21% | 56.72% | 60.67% |
The prefix Wiki (Wikipedia corpus) or Bio (BioASQ dataset) refers to the corpus used to train the word embedding model. The label bow (CBOW) or skip (skip-gram) refers to the type of architecture used to build the model. The number preceding w and n indicates the size of the context window and the negative sampling, respectively.