Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Terminology-based Text Embedding for Computing Document Similarities on
  Technical Content   We propose in this paper a new, hybrid document embedding approach in order
to address the problem of document similarities with respect to the technical
content. To do so, we employ a state-of-the-art graph techniques to first
extract the keyphrases (composite keywords) of documents and, then, use them to
score the sentences. Using the ranked sentences, we propose two approaches to
embed documents and show their performances with respect to two baselines. With
domain expert annotations, we illustrate that the proposed methods can find
more relevant documents and outperform the baselines up to 27% in terms of
NDCG.

33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol