Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Simple dynamic word embeddings for mapping perceptions in the public
  sphere   Word embeddings trained on large-scale historical corpora can illuminate
human biases and stereotypes that perpetuate social inequalities. These
embeddings are often trained in separate vector space models defined according
to different attributes of interest. In this paper, we develop a unified
dynamic embedding model that learns attribute-specific word embeddings. We
apply our model to investigate i) 20th century gender and ethnic occupation
biases embedded in the Corpus of Historical American English (COHA), and ii)
biases against refugees embedded in a novel corpus of talk radio transcripts
containing 119 million words produced over one month across 83 stations and 64
cities. Our results shed preliminary light on scenarios when dynamic embedding
models may be more suitable for representing linguistic biases than individual
vector space models, and vice-versa.

22
The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
16
The categories of this paper are: stat
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin