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        <title>Low-Rank Approximations of Second-Order Document Representations</title>
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            <namePart type="given">Mohit</namePart>
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    <abstract>Document embeddings, created with methods ranging from simple heuristics to statistical and deep models, are widely applicable. Bag-of-vectors models for documents include the mean and quadratic approaches (Torki, 2018). We present evidence that quadratic statistics alone, without the mean information, can offer superior accuracy, fast document comparison, and compact document representations. In matching news articles to their comment threads, low-rank representations of only 3-4 times the size of the mean vector give most accurate matching, and in standard sentence comparison tasks, results are state of the art despite faster computation. Similarity measures are discussed, and the Frobenius product implicit in the proposed method is contrasted to Wasserstein or Bures metric from the transportation theory. We also shortly demonstrate matching of unordered word lists to documents, to measure topicality or sentiment of documents.</abstract>
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    <identifier type="doi">10.18653/v1/K19-1059</identifier>
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%0 Conference Proceedings
%T Low-Rank Approximations of Second-Order Document Representations
%A Lagus, Jarkko
%A Sinkkonen, Janne
%A Klami, Arto
%Y Bansal, Mohit
%Y Villavicencio, Aline
%S Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F lagus-etal-2019-low
%X Document embeddings, created with methods ranging from simple heuristics to statistical and deep models, are widely applicable. Bag-of-vectors models for documents include the mean and quadratic approaches (Torki, 2018). We present evidence that quadratic statistics alone, without the mean information, can offer superior accuracy, fast document comparison, and compact document representations. In matching news articles to their comment threads, low-rank representations of only 3-4 times the size of the mean vector give most accurate matching, and in standard sentence comparison tasks, results are state of the art despite faster computation. Similarity measures are discussed, and the Frobenius product implicit in the proposed method is contrasted to Wasserstein or Bures metric from the transportation theory. We also shortly demonstrate matching of unordered word lists to documents, to measure topicality or sentiment of documents.
%R 10.18653/v1/K19-1059
%U https://aclanthology.org/K19-1059
%U https://doi.org/10.18653/v1/K19-1059
%P 634-644

Markdown (Informal)

[Low-Rank Approximations of Second-Order Document Representations](https://aclanthology.org/K19-1059) (Lagus et al., CoNLL 2019)

• Low-Rank Approximations of Second-Order Document Representations (Lagus et al., CoNLL 2019)

ACL

• Jarkko Lagus, Janne Sinkkonen, and Arto Klami. 2019. Low-Rank Approximations of Second-Order Document Representations. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 634–644, Hong Kong, China. Association for Computational Linguistics.