Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automatically Inferring Gender Associations from Language   In this paper, we pose the question: do people talk about women and men in
different ways? We introduce two datasets and a novel integration of approaches
for automatically inferring gender associations from language, discovering
coherent word clusters, and labeling the clusters for the semantic concepts
they represent. The datasets allow us to compare how people write about women
and men in two different settings - one set draws from celebrity news and the
other from student reviews of computer science professors. We demonstrate that
there are large-scale differences in the ways that people talk about women and
men and that these differences vary across domains. Human evaluations show that
our methods significantly outperform strong baselines.

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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ex
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The categories of this paper are: econ
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The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
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The categories of this paper are: eess
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The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: gr-qc
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ph
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The categories of this paper are: physics
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The categories of this paper are: hep-lat
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The categories of this paper are: funct-an