Word embedding models excel in measuring word similarity and completing analogies. Word embeddings based on different notions of context trade off strengths in one area for weaknesses in another. Linear bag-of-words contexts, such as in word2vec, can capture topical similarity better, while dependency-based word embeddings better encode functional similarity. By combining these two word embeddings using different metrics, we show how the best aspects of both approaches can be captured. We show state-of-the-art performance on standard word and relational similarity benchmarks.

中文翻译：

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词和关系相似度的双重嵌入和度量

词嵌入模型在测量词相似性和完成类比方面表现出色。基于不同上下文概念的词嵌入会在一个领域的优势与另一个领域的弱点进行权衡。线性词袋上下文，例如 word2vec，可以更好地捕捉主题相似性，而基于依赖的词嵌入可以更好地编码功能相似性。通过使用不同的指标组合这两个词嵌入，我们展示了如何捕捉这两种方法的最佳方面。我们在标准词和关系相似性基准上展示了最先进的性能。