Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Opposite of Smoothing: A Language Model Approach to Ranking
  Query-Specific Document Clusters   Exploiting information induced from (query-specific) clustering of
top-retrieved documents has long been proposed as a means for improving
precision at the very top ranks of the returned results. We present a novel
language model approach to ranking query-specific clusters by the presumed
percentage of relevant documents that they contain. While most previous cluster
ranking approaches focus on the cluster as a whole, our model utilizes also
information induced from documents associated with the cluster. Our model
substantially outperforms previous approaches for identifying clusters
containing a high relevant-document percentage. Furthermore, using the model to
produce document ranking yields precision-at-top-ranks performance that is
consistently better than that of the initial ranking upon which clustering is
performed. The performance also favorably compares with that of a
state-of-the-art pseudo-feedback-based retrieval method.

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The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom