Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Linguistic Geometries for Unsupervised Dimensionality Reduction   Text documents are complex high dimensional objects. To effectively visualize
such data it is important to reduce its dimensionality and visualize the low
dimensional embedding as a 2-D or 3-D scatter plot. In this paper we explore
dimensionality reduction methods that draw upon domain knowledge in order to
achieve a better low dimensional embedding and visualization of documents. We
consider the use of geometries specified manually by an expert, geometries
derived automatically from corpus statistics, and geometries computed from
linguistic resources.

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The categories of this paper are: gr-qc
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-th
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The categories of this paper are: eess
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The categories of this paper are: plasm-ph
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The categories of this paper are: nlin
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-ex
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
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The categories of this paper are: q-alg
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The categories of this paper are: patt-sol
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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: chem-ph
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The categories of this paper are: astro-ph