Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cross-domain Aspect Category Transfer and Detection via Traceable
  Heterogeneous Graph Representation Learning   Aspect category detection is an essential task for sentiment analysis and
opinion mining. However, the cost of categorical data labeling, e.g., label the
review aspect information for a large number of product domains, can be
inevitable but unaffordable. In this study, we propose a novel problem,
cross-domain aspect category transfer and detection, which faces three
challenges: various feature spaces, different data distributions, and diverse
output spaces. To address these problems, we propose an innovative solution,
Traceable Heterogeneous Graph Representation Learning (THGRL). Unlike prior
text-based aspect detection works, THGRL explores latent domain aspect category
connections via massive user behavior information on a heterogeneous graph.
Moreover, an innovative latent variable "Walker Tracer" is introduced to
characterize the global semantic/aspect dependencies and capture the
informative vertexes on the random walk paths. By using THGRL, we project
different domains' feature spaces into a common one, while allowing data
distributions and output spaces stay differently. Experiment results show that
the proposed method outperforms a series of state-of-the-art baseline models.

21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
30
The categories of this paper are: q-alg
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an