Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Keyphrase Extraction Using Neighborhood Knowledge Based on Word
  Embeddings   Keyphrase extraction is the task of finding several interesting phrases in a
text document, which provide a list of the main topics within the document.
Most existing graph-based models use co-occurrence links as cohesion indicators
to model the relationship of syntactic elements. However, a word may have
different forms of expression within the document, and may have several
synonyms as well. Simply using co-occurrence information cannot capture this
information. In this paper, we enhance the graph-based ranking model by
leveraging word embeddings as background knowledge to add semantic information
to the inter-word graph. Our approach is evaluated on established benchmark
datasets and empirical results show that the word embedding neighborhood
information improves the model performance.

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The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
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The categories of this paper are: math-ph
9
The categories of this paper are: nlin
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The categories of this paper are: chao-dyn
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The categories of this paper are: eess
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The categories of this paper are: patt-sol
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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: supr-con
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The categories of this paper are: math
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-th
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The categories of this paper are: atom-ph