short-paper

CGTR: Convolution Graph Topology Representation for Document Ranking

Authors:

Jun GuoAuthors Info & Claims

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Pages 2173 - 2176

https://doi.org/10.1145/3340531.3412073

Published: 19 October 2020 Publication History

Get Access

Abstract

Contextualized neural language models have gained much attention in Information Retrieval (IR) with its ability to achieve better text understanding by capturing contextual structure. However, to achieve better document understanding, it is necessary to involve global structure of a document. In this paper, we take the advantage of Graph Convolutional Networks (GCN) to model global word-relation structure of a document to improve context-aware document ranking. We propose to build a graph for a document to model the global structure. The nodes and edges of the graph are constructed from contextual embeddings. Then we apply graph convolution on the graph to learning a new representation, and this representation covers both contextual and global structure information. The experimental results show that our method outperforms the state-of-the-art contextual language models, which demonstrate that incorporating global structure is useful for improving document ranking and GCN is an effective way to achieve it.

Supplementary Material

MP4 File (3340531.3412073.mp4)

In this paper, we firstly bring Graph Convolutional Networks in the IR task. We propose two deeper representations (feature representations and graph topology representations) from contextualized language model for IR task. We propose to build a graph for a document to model the global structure containing nodes and edges based on contextual embeddings.

Download
84.87 MB

References

[1]

J. Bastings, I. Titov, W. Aziz, D. Marcheggiani, and K. Simaan. 2017. Graph convolutional encoders for syntax-aware neural machine translation. In arXiv preprint arXiv:1704.04675.

Google Scholar

[2]

P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al. 2018. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261 (2018).

Google Scholar

[3]

H. Cai, V. W. Zheng, and K. Chen-Chuan Chang. 2018. A comprehensive survey of graph embedding: Problems, techniques and applications. IEEE Transactions on Knowledge and Data Engineering 30, 9 (2018), 1616--1637.

Digital Library

Google Scholar

[4]

Z. Dai, J. Callan C. Xiong, and Z. Liu. 2018. Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search. In WSDM.

Google Scholar

[5]

Z. Dai and J. Callan. 2019. Deeper text understanding for IR with contextual neural language modeling. In SIGIR.

Google Scholar

[6]

J. Guo, Y. Fan, Q. Ai, and W. B. Croft. 2016. Deep Relevance Matching Model for Ad-hoc Retrieval. In CIKM.

Google Scholar

[7]

K. Hui, A. Yates, K. Berberich, and G. de Melo. 2018. Co-PACRR:A Context-Aware Neural IR Model for Ad-hoc Retrieval. In WSDM.

Google Scholar

[8]

T. N. Kipf and M. Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR.

Google Scholar

[9]

Y. Li, R. Jin, and Y. Luo. 2019. Classifying relations in clinical narratives using segment graph convolutional and recurrent neural networks (Seg-GCRNs). Journal of the American Medical Informatics Association 26, 3 (2019), 262--268.

Crossref

Google Scholar

[10]

B. Liu, T. Zhang, D. Niu, J. Lin, K. Lai, and Y. Xu. 2019. Matching Long Text Documents via Graph Convolutional Networks. In ACL.

Google Scholar

[11]

S. MacAvaney, A. Yates, A. Cohan, and N. Goharian. 2019. CEDR: Contextualized Embeddings for Document Ranking. In SIGIR.

Digital Library

Google Scholar

[12]

Y. Qi, J. Zhang, W. Xu, and J. Guo. 2020. Salient context-based semantic matching for information retrieval. EURASIP Journal on Advances in Signal Processing 2020 (2020). https://doi.org/10.1186/s13634-020-00688--1

Crossref

Google Scholar

[13]

W. Yang, H. Zhang, and J. Lin. 2019. Simple applications of BERT for ad hoc document retrieval. In SIGIR.

Google Scholar

[14]

L. Yao, C. Mao, and Y. Luo. 2019. Graph convolutional networks for text classification. In AAAI.

Google Scholar

Cited By

View all

Horikawa SNemoto CTajima KMatsubara MMorishima A(2024)An Adaptive Feature Selection Method for Learning-to-Enumerate ProblemAdvances in Information Retrieval10.1007/978-3-031-56063-7_8(122-136)Online publication date: 23-Mar-2024
https://doi.org/10.1007/978-3-031-56063-7_8
Shan MMa YRuan SCao ZTong SLiu QSu YWang SFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)PaperLM: A Pre-trained Model for Hierarchical Examination Paper Representation LearningProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615003(2178-2187)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615003
Keyhanipour A(2023)Graph-based comparative analysis of learning to rank datasetsInternational Journal of Data Science and Analytics10.1007/s41060-023-00406-8Online publication date: 30-Jun-2023
https://doi.org/10.1007/s41060-023-00406-8
Show More Cited By

Index Terms

CGTR: Convolution Graph Topology Representation for Document Ranking
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Learning to rank

Recommendations

Deeper Text Understanding for IR with Contextual Neural Language Modeling
SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval

Neural networks provide new possibilities to automatically learn complex language patterns and query-document relations. Neural IR models have achieved promising results in learning query-document relevance patterns, but few explorations have been done ...
DGE-GSIM: A multi-task dual graph embedding learning for graph similarity computation
ICMLSC '22: Proceedings of the 2022 6th International Conference on Machine Learning and Soft Computing

Graph similarity estimation is a challenging task due to the complex graph structure. To achieve an exact similarity estimation for input graphs, two critical factors are how to learn an appropriate graph embedding and how to compute the similarity ...
Graph Convolutional Networks with EigenPooling
KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Graph neural networks, which generalize deep neural network models to graph structured data, have attracted increasing attention in recent years. They usually learn node representations by transforming, propagating and aggregating node features and have ...

Comments

Information & Contributors

Information

Published In

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

October 2020

3619 pages

ISBN:9781450368599

DOI:10.1145/3340531

General Chairs:
Mathieu d'Aquin
DSI, Insight, NUI Galway, Ireland
,
Stefan Dietze
GESIS, Cologne, Germany, Heinrich-Heine-University Düsseldorf, Germany, L3S Research Center, Germany
,
Program Chairs:
Claudia Hauff
TU Delft, The Netherlands
,
Edward Curry
DSI, Insight, NUI Galway, Ireland
,
Philippe Cudre Mauroux
eXascale, University of Fribourg, Switzerland

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 October 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Funding Sources

the Beijing Natural Science Foundation
the Fundamental Research Funds for the Central Universities
the National Natural Science Foundation of China
Beijing Municipal Postdoctoral Foundation, Chaoyang District Postdoctoral Foundation

Conference

CIKM '20

Sponsor:

CIKM '20: The 29th ACM International Conference on Information and Knowledge Management

October 19 - 23, 2020

Virtual Event, Ireland

Acceptance Rates

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

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
193
Total Downloads

Downloads (Last 12 months)14
Downloads (Last 6 weeks)0

Reflects downloads up to 22 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Horikawa SNemoto CTajima KMatsubara MMorishima A(2024)An Adaptive Feature Selection Method for Learning-to-Enumerate ProblemAdvances in Information Retrieval10.1007/978-3-031-56063-7_8(122-136)Online publication date: 23-Mar-2024
https://doi.org/10.1007/978-3-031-56063-7_8
Shan MMa YRuan SCao ZTong SLiu QSu YWang SFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)PaperLM: A Pre-trained Model for Hierarchical Examination Paper Representation LearningProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615003(2178-2187)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615003
Keyhanipour A(2023)Graph-based comparative analysis of learning to rank datasetsInternational Journal of Data Science and Analytics10.1007/s41060-023-00406-8Online publication date: 30-Jun-2023
https://doi.org/10.1007/s41060-023-00406-8
Albarede LMulhem PGoeuriot LMarié SLe Pape-Gardeux CChardin-Segui T(2023)Heterogeneous graph attention networks for passage retrievalInformation Retrieval10.1007/s10791-023-09424-326:1-2Online publication date: 16-Nov-2023
https://dl.acm.org/doi/10.1007/s10791-023-09424-3
Fan LLi QLiu BWu XZhang XLv FLin GLi SJin TYang K(2022)Modeling User Behavior with Graph Convolution for Personalized Product SearchProceedings of the ACM Web Conference 202210.1145/3485447.3511949(203-212)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3485447.3511949
Qi YZhang JXu WGuo JZhang H(2020)Learning Graph Topology Representation with Attention Networks2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)10.1109/VCIP49819.2020.9301864(1-4)Online publication date: 1-Dec-2020
https://doi.org/10.1109/VCIP49819.2020.9301864

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Deeper Text Understanding for IR with Contextual Neural Language Modeling

DGE-GSIM: A multi-task dual graph embedding learning for graph similarity computation

Graph Convolutional Networks with EigenPooling