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Correlation Coefficients and Semantic Textual Similarity
2019
Proceedings of the 2019 Conference of the North
A large body of research into semantic textual similarity has focused on constructing state-of-the-art embeddings using sophisticated modelling, careful choice of learning signals and many clever tricks. By contrast, little attention has been devoted to similarity measures between these embeddings, with cosine similarity being used unquestionably in the majority of cases. In this work, we illustrate that for all common word vectors, cosine similarity is essentially equivalent to the Pearson
doi:10.18653/v1/n19-1100
dblp:conf/naacl/ZhelezniakSSH19
fatcat:wkelvwkcg5h7faum6oec7fpjxq