Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Context Retrieval for Long-tail Entities   Monitoring entities in media streams often relies on rich entity
representations, like structured information available in a knowledge base
(KB). For long-tail entities, such monitoring is highly challenging, due to
their limited, if not entirely missing, representation in the reference KB. In
this paper, we address the problem of retrieving textual contexts for
monitoring long-tail entities. We propose an unsupervised method to overcome
the limited representation of long-tail entities by leveraging established
entities and their contexts as support information. Evaluation on a
purpose-built test collection shows the suitability of our approach and its
robustness for out-of-KB entities.

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The categories of this paper are: nlin
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The categories of this paper are: supr-con
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
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The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
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The categories of this paper are: patt-sol
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The categories of this paper are: math-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: econ
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The categories of this paper are: math
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The categories of this paper are: physics
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The categories of this paper are: stat
20
The categories of this paper are: acc-phys