Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Selective Term Proximity Scoring Via BP-ANN   When two terms occur together in a document, the probability of a close
relationship between them and the document itself is greater if they are in
nearby positions. However, ranking functions including term proximity (TP)
require larger indexes than traditional document-level indexing, which slows
down query processing. Previous studies also show that this technique is not
effective for all types of queries. Here we propose a document ranking model
which decides for which queries it would be beneficial to use a proximity-based
ranking, based on a collection of features of the query. We use a machine
learning approach in determining whether utilizing TP will be beneficial.
Experiments show that the proposed model returns improved rankings while also
reducing the overhead incurred as a result of using TP statistics.

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The categories of this paper are: hep-ph
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The categories of this paper are: patt-sol
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
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The categories of this paper are: math
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
3
The categories of this paper are: physics
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The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys