Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Query Understanding via Entity Attribute Identification   Understanding searchers' queries is an essential component of semantic search
systems. In many cases, search queries involve specific attributes of an entity
in a knowledge base (KB), which can be further used to find query answers. In
this study, we aim to move forward the understanding of queries by identifying
their related entity attributes from a knowledge base. To this end, we
introduce the task of entity attribute identification and propose two methods
to address it: (i) a model based on Markov Random Field, and (ii) a learning to
rank model. We develop a human annotated test collection and show that our
proposed methods can bring significant improvements over the baseline methods.

25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
16
The categories of this paper are: stat
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex