Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ClassiNet -- Predicting Missing Features for Short-Text Classification   The fundamental problem in short-text classification is \emph{feature
sparseness} -- the lack of feature overlap between a trained model and a test
instance to be classified. We propose \emph{ClassiNet} -- a network of
classifiers trained for predicting missing features in a given instance, to
overcome the feature sparseness problem. Using a set of unlabeled training
instances, we first learn binary classifiers as feature predictors for
predicting whether a particular feature occurs in a given instance. Next, each
feature predictor is represented as a vertex $v_i$ in the ClassiNet where a
one-to-one correspondence exists between feature predictors and vertices. The
weight of the directed edge $e_{ij}$ connecting a vertex $v_i$ to a vertex
$v_j$ represents the conditional probability that given $v_i$ exists in an
instance, $v_j$ also exists in the same instance. We show that ClassiNets
generalize word co-occurrence graphs by considering implicit co-occurrences
between features. We extract numerous features from the trained ClassiNet to
overcome feature sparseness. In particular, for a given instance $\vec{x}$, we
find similar features from ClassiNet that did not appear in $\vec{x}$, and
append those features in the representation of $\vec{x}$. Moreover, we propose
a method based on graph propagation to find features that are indirectly
related to a given short-text. We evaluate ClassiNets on several benchmark
datasets for short-text classification. Our experimental results show that by
using ClassiNet, we can statistically significantly improve the accuracy in
short-text classification tasks, without having to use any external resources
such as thesauri for finding related features.

26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
10
The categories of this paper are: q-bio
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
23
The categories of this paper are: solv-int
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph