word2vec论文

最新推荐文章于 2024-02-20 17:09:35 发布

qq_26595557

最新推荐文章于 2024-02-20 17:09:35 发布

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本文链接：https://blog.youkuaiyun.com/qq_26595557/article/details/86466976

本文介绍了word2vec模型的扩展，通过词频下采样提高训练速度和词向量质量。提出了负采样作为Hierarchical Softmax的替代方案。此外，讨论了词序不敏感的问题，并提出了一种识别短语的方法，使得学习大量短语的向量表示成为可能。

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Abstract

The recently introduced continuous Skip-gram model is an efficient method for learning high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships. In this paper we present several extensions that improve both the quality of the vectors and the training speed. By subsampling of the frequent words we obtain significant speedup and also learn more regular word representations. We also describe a simple alternative to the hierarchical softmax called negative sampling. An inherent limitation of word representations is their indifference to word order and their inability to represent idiomatic phrases. For example, the meanings of “Canada” and “Air” cannot be easily combined to obtain “Air Canada”. Motivated b