research-article

Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation Learning

Authors:

Xiaozhong LiuAuthors Info & Claims

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Pages 289 - 298

https://doi.org/10.1145/3357384.3357989

Published: 03 November 2019 Publication History

Abstract

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.

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Cited By

Chebolu SRosso PKar SSolorio T(2022)Survey on Aspect Category DetectionACM Computing Surveys10.1145/354455755:7(1-37)Online publication date: 15-Dec-2022
https://dl.acm.org/doi/10.1145/3544557
Li SLi YZhao WDing BWen J(2021)Interpretable Aspect-Aware Capsule Network for Peer Review Based Citation Count PredictionACM Transactions on Information Systems10.1145/346664040:1(1-29)Online publication date: 24-Nov-2021
https://dl.acm.org/doi/10.1145/3466640
Xu ZXu JChai LZhao WFan Y(2021)Unsupervised heterogeneous transfer fault diagnosis based on graph Laplacian common subspace2021 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN52387.2021.9534238(1-8)Online publication date: 18-Jul-2021
https://doi.org/10.1109/IJCNN52387.2021.9534238
Show More Cited By

Index Terms

Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation Learning
1. Applied computing
  1. Enterprise computing
    1. Business process management
      1. Business intelligence
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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cover image ACM Conferences

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

November 2019

3373 pages

ISBN:9781450369763

DOI:10.1145/3357384

General Chairs:
Wenwu Zhu
Tsinghua University, China
,
Dacheng Tao
University of Massachusetts, USA
,
Xueqi Cheng
Institute of Computing Technology, CAS, China
,
Program Chairs:
Peng Cui
Tsinghua University, China
,
Elke Rundensteiner
Worcester Polytechnic Institute, USA
,
David Carmel
Amazon Research, USA
,
Qi He
LinkedIn, USA
,
Jeffrey Xu Yu
Chinese University of Hong Kong, China

Copyright © 2019 ACM.

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Published: 03 November 2019

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China Department of Science and Technology Key Grant
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities

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CIKM '19

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CIKM '19: The 28th ACM International Conference on Information and Knowledge Management

November 3 - 7, 2019

Beijing, China

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CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

Chebolu SRosso PKar SSolorio T(2022)Survey on Aspect Category DetectionACM Computing Surveys10.1145/354455755:7(1-37)Online publication date: 15-Dec-2022
https://dl.acm.org/doi/10.1145/3544557
Li SLi YZhao WDing BWen J(2021)Interpretable Aspect-Aware Capsule Network for Peer Review Based Citation Count PredictionACM Transactions on Information Systems10.1145/346664040:1(1-29)Online publication date: 24-Nov-2021
https://dl.acm.org/doi/10.1145/3466640
Xu ZXu JChai LZhao WFan Y(2021)Unsupervised heterogeneous transfer fault diagnosis based on graph Laplacian common subspace2021 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN52387.2021.9534238(1-8)Online publication date: 18-Jul-2021
https://doi.org/10.1109/IJCNN52387.2021.9534238
Jiang ZGao ZLan JYang HLu YLiu X(2020)Task-Oriented Genetic Activation for Large-Scale Complex Heterogeneous Graph EmbeddingProceedings of The Web Conference 202010.1145/3366423.3380230(1581-1591)Online publication date: 20-Apr-2020
https://dl.acm.org/doi/10.1145/3366423.3380230

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