Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Inducing Language Networks from Continuous Space Word Representations   Recent advancements in unsupervised feature learning have developed powerful
latent representations of words. However, it is still not clear what makes one
representation better than another and how we can learn the ideal
representation. Understanding the structure of latent spaces attained is key to
any future advancement in unsupervised learning. In this work, we introduce a
new view of continuous space word representations as language networks. We
explore two techniques to create language networks from learned features by
inducing them for two popular word representation methods and examining the
properties of their resulting networks. We find that the induced networks
differ from other methods of creating language networks, and that they contain
meaningful community structure.

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The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
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The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
3
The categories of this paper are: physics
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The categories of this paper are: q-alg
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The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: nlin
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The categories of this paper are: nucl-th