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5 months ago
Neural sentence embedding models for semantic similarity estimation in the biomedical domain
Kathrin Blagec Hong Xu Asan Agibetov Matthias Samwald
Abstract
Code Repositories
Benchmarks
| Benchmark | Methodology | Metrics |
|---|---|---|
| sentence-embeddings-for-biomedical-texts-on | Q-gram (q = 3) | Pearson Correlation: 0.723 |
| sentence-embeddings-for-biomedical-texts-on | Paragraph vector (PV-DBOW) | Pearson Correlation: 0.804 |
| sentence-embeddings-for-biomedical-texts-on | Supervised combination of: Jaccard, Q-gram, sent2vec, Paragraph vector DM, skip-thoughts, fastText | Pearson Correlation: 0.871 |
| sentence-embeddings-for-biomedical-texts-on | Sent2vec | Pearson Correlation: 0.798 |
| sentence-embeddings-for-biomedical-texts-on | Skip-thoughts | Pearson Correlation: 0.485 |
| sentence-embeddings-for-biomedical-texts-on | fastText (skip-gram, max pooling) | Pearson Correlation: 0.766 |
| sentence-embeddings-for-biomedical-texts-on | Paragraph vector (PV-DM) | Pearson Correlation: 0.819 |
| sentence-embeddings-for-biomedical-texts-on | Unsupervised combination (mean) of: Jaccard, q-gram, Paragraph vector (PV-DBOW) and sent2vec | Pearson Correlation: 0.846 |
| sentence-embeddings-for-biomedical-texts-on | fastText (CBOW, max pooling) | Pearson Correlation: 0.253 |
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