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Sentence Embeddings For Biomedical Texts On

评估指标

Pearson Correlation

评测结果

各个模型在此基准测试上的表现结果

Paper Title
Supervised combination of: Jaccard, Q-gram, sent2vec, Paragraph vector DM, skip-thoughts, fastText0.871Neural sentence embedding models for semantic similarity estimation in the biomedical domain
Unsupervised combination (mean) of: Jaccard, q-gram, Paragraph vector (PV-DBOW) and sent2vec0.846Neural sentence embedding models for semantic similarity estimation in the biomedical domain
Paragraph vector (PV-DM)0.819Neural sentence embedding models for semantic similarity estimation in the biomedical domain
BioSentVec (PubMed)0.817BioSentVec: creating sentence embeddings for biomedical texts
Paragraph vector (PV-DBOW)0.804Neural sentence embedding models for semantic similarity estimation in the biomedical domain
Sent2vec0.798Neural sentence embedding models for semantic similarity estimation in the biomedical domain
BioSentVec (PubMed + MIMIC-III)0.795BioSentVec: creating sentence embeddings for biomedical texts
Paragraph Vector0.787BIOSSES: A Semantic Sentence Similarity Estimation System for the Biomedical Domain
fastText (skip-gram, max pooling)0.766Neural sentence embedding models for semantic similarity estimation in the biomedical domain
Q-gram (q = 3)0.723Neural sentence embedding models for semantic similarity estimation in the biomedical domain
Skip-thoughts0.485Neural sentence embedding models for semantic similarity estimation in the biomedical domain
BioSentVec (MIMIC-III)0.350BioSentVec: creating sentence embeddings for biomedical texts
Universal Sentence Encoder0.345BioSentVec: creating sentence embeddings for biomedical texts
fastText (CBOW, max pooling)0.253Neural sentence embedding models for semantic similarity estimation in the biomedical domain
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Sentence Embeddings For Biomedical Texts On | SOTA | HyperAI超神经