Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Visualizing Linguistic Shift   Neural network based models are a very powerful tool for creating word
embeddings, the objective of these models is to group similar words together.
These embeddings have been used as features to improve results in various
applications such as document classification, named entity recognition, etc.
Neural language models are able to learn word representations which have been
used to capture semantic shifts across time and geography. The objective of
this paper is to first identify and then visualize how words change meaning in
different text corpus. We will train a neural language model on texts from a
diverse set of disciplines philosophy, religion, fiction etc. Each text will
alter the embeddings of the words to represent the meaning of the word inside
that text. We will present a computational technique to detect words that
exhibit significant linguistic shift in meaning and usage. We then use enhanced
scatterplots and storyline visualization to visualize the linguistic shift.

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The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
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The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org