论文标题
记录矢量空间密度的相似性
Document Similarity from Vector Space Densities
论文作者
论文摘要
我们提出了一种计算光方法，用于估计文本文档之间的相似性，我们称之为密度相似（DS）方法。该方法基于在高维欧几里得空间和内核回归中嵌入的单词，并考虑了单词之间的语义关系。我们发现，该方法的准确性几乎与最先进的方法相同，而速度的增长非常大。此外，我们介绍了TOP-K精度度量的广义版本和相似性模型之间一致性的JACCARD度量。
We propose a computationally light method for estimating similarities between text documents, which we call the density similarity (DS) method. The method is based on a word embedding in a high-dimensional Euclidean space and on kernel regression, and takes into account semantic relations among words. We find that the accuracy of this method is virtually the same as that of a state-of-the-art method, while the gain in speed is very substantial. Additionally, we introduce generalized versions of the top-k accuracy metric and of the Jaccard metric of agreement between similarity models.
