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Table 3 Average estimated cosine similarities for sentence pairs included in the negation and antonym subset and a reference set of highly similar sentences per model. Lower values indicate lower estimated semantic similarity; higher values indicate higher estimated semantic similarities

From: Neural sentence embedding models for semantic similarity estimation in the biomedical domain

 

Sent2vec

Skip-thoughts

PV-DM

PV-DBOW

fastText CBOW

fastText skip-gram

Subset of highly similar sentences (n = 11)

0.706

0.899

0.652

0.568

0.938

0.971

Negation subset (n = 13)

0.967

0.999

0.930

0.936

0.945

0.979

Antonym subset (n = 7)

0.983

0.999

0.968

0.960

0.976

0.989

  1. PV-DM Paragraph Vector Distributed Memory, PV-DBOW Paragraph Vector Distributed Bag of Words, CBOW Continuous Bag of Words