Document Similarity from Vector Space Densities

• URL: http://arxiv.org/abs/2009.00672v1
• Date: Tue, 1 Sep 2020 19:28:51 GMT
• Title: Document Similarity from Vector Space Densities
• Authors: Ilia Rushkin
• Abstract summary: We propose a method for estimating similarities between text documents. The method is based on a word embedding in a high-dimensional Euclidean space and on kernel regression. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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