Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Good, Better, Best: Choosing Word Embedding Context   We propose two methods of learning vector representations of words and
phrases that each combine sentence context with structural features extracted
from dependency trees. Using several variations of neural network classifier,
we show that these combined methods lead to improved performance when used as
input features for supervised term-matching.

37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
21
The categories of this paper are: adap-org
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The categories of this paper are: stat
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
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The categories of this paper are: chao-dyn
12
The categories of this paper are: nucl-th
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The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas