Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Analyzing Scientific Publications using Domain-Specific Word Embedding
  and Topic Modelling   The scientific world is changing at a rapid pace, with new technology being
developed and new trends being set at an increasing frequency. This paper
presents a framework for conducting scientific analyses of academic
publications, which is crucial to monitor research trends and identify
potential innovations. This framework adopts and combines various techniques of
Natural Language Processing, such as word embedding and topic modelling. Word
embedding is used to capture semantic meanings of domain-specific words. We
propose two novel scientific publication embedding, i.e., PUB-G and PUB-W,
which are capable of learning semantic meanings of general as well as
domain-specific words in various research fields. Thereafter, topic modelling
is used to identify clusters of research topics within these larger research
fields. We curated a publication dataset consisting of two conferences and two
journals from 1995 to 2020 from two research domains. Experimental results show
that our PUB-G and PUB-W embeddings are superior in comparison to other
baseline embeddings by a margin of ~0.18-1.03 based on topic coherence.

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The categories of this paper are: q-bio
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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: nucl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
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The categories of this paper are: solv-int
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The categories of this paper are: ao-sci
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg
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The categories of this paper are: math-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: nlin
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The categories of this paper are: atom-ph
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The categories of this paper are: econ