Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Text classification with word embedding regularization and soft
  similarity measure   Since the seminal work of Mikolov et al., word embeddings have become the
preferred word representations for many natural language processing tasks.
Document similarity measures extracted from word embeddings, such as the soft
cosine measure (SCM) and the Word Mover's Distance (WMD), were reported to
achieve state-of-the-art performance on semantic text similarity and text
classification.
  Despite the strong performance of the WMD on text classification and semantic
text similarity, its super-cubic average time complexity is impractical. The
SCM has quadratic worst-case time complexity, but its performance on text
classification has never been compared with the WMD. Recently, two word
embedding regularization techniques were shown to reduce storage and memory
costs, and to improve training speed, document processing speed, and task
performance on word analogy, word similarity, and semantic text similarity.
However, the effect of these techniques on text classification has not yet been
studied.
  In our work, we investigate the individual and joint effect of the two word
embedding regularization techniques on the document processing speed and the
task performance of the SCM and the WMD on text classification. For evaluation,
we use the $k$NN classifier and six standard datasets: BBCSPORT, TWITTER,
OHSUMED, REUTERS-21578, AMAZON, and 20NEWS.
  We show 39% average $k$NN test error reduction with regularized word
embeddings compared to non-regularized word embeddings. We describe a practical
procedure for deriving such regularized embeddings through Cholesky
factorization. We also show that the SCM with regularized word embeddings
significantly outperforms the WMD on text classification and is over 10,000
times faster.

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The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: mtrl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: hep-th
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The categories of this paper are: patt-sol
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The categories of this paper are: quant-ph
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The categories of this paper are: cmp-lg
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The categories of this paper are: supr-con
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The categories of this paper are: nucl-th
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The categories of this paper are: alg-geom
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The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: chao-dyn
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The categories of this paper are: solv-int