Correlations between Word Vector Sets

IJCNLP 2019 Vitalii ZhelezniakApril ShenDaniel BusbridgeAleksandar SavkovNils Hammerla

Similarity measures based purely on word embeddings are comfortably competing with much more sophisticated deep learning and expert-engineered systems on unsupervised semantic textual similarity (STS) tasks. In contrast to commonly used geometric approaches, we treat a single word embedding as e.g. 300 observations from a scalar random variable... (read more)

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