Deep Learning on Graphs for Natural Language Processing
Introduction
Due to its great power in modeling non-Euclidean data like graphs or manifolds, deep learning on graph techniques (i.e., Graph Neural Networks (GNNs)) have opened a new door to solving challenging graph-related NLP problems. There has seen a surge of interests in applying deep learning on graph techniques to NLP, and has achieved considerable success in many NLP tasks, ranging from classification tasks like sentence classification, semantic role labeling and relation extraction, to generation tasks like machine translation, question generation and summarization. Despite these successes, deep learning on graphs for NLP still face many challenges, including automatically transforming original text sequence data into highly graph-structured data, and effectively modeling complex data that involves mapping between graph-based inputs and other highly structured output data such as sequences, trees, and graph data with multi-types in both nodes and edges.
This project aims to develop efficient graph representation, learning, and reasoning techniques for natural language processing tasks.
Relevant Materials
Publications
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Bang Liu, Lingfei Wu. (2021). Graph Neural Networks in Natural Language Processing, In: Graph Neural Networks: Foundations, Frontiers, and Applications, Lingfei Wu, Peng Cui, Jian Pei, Liang Zhao. (Chapter) (Book)
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Bang Liu (2020). Natural Language Processing and Text Mining with Graph-structured Representations, PhD thesis, Advisor: Di Niu, Linglong Kong, University of Alberta, January 2020. (Download)
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Jiayuan Ding*, Tong Xiang*, Zijing Ou*, Wangyang Zuo, Ruihui Zhao, Chenhua Lin, Yefeng Zheng, Bang Liu✉. (2022).
Tell Me How to Survey: Literature Review Made Simple with Automatic Reading Path Generation,
in ICDE 2022.
(Download) (SurveyBank Dataset)
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Zijing Ou+, Qinliang Su✉, Jianxing Yu, Bang Liu, Jingwen Wang, Ruihui Zhao, Changyou Chen, Yefeng Zheng. (2021).
Integrating Semantics and Neighborhood Information with Graph-Driven Generative Models for Document Retrieval,
in ACL 2021.
(Download)
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Bang Liu*, Weidong Guo*, Di Niu, Jingwen Luo, Chaoyue Wang, Zhen Wen, Yu Xu. (2020).
GIANT: Scalable Creation of a Web-scale Ontology,
in SIGMOD 2020.
(Download)
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Bang Liu, Di Niu, Haojie Wei, Jinghong Lin, Yancheng He, Kunfeng Lai, Yu Xu. (2019).
Matching Article Pairs with Graphical Decomposition and Convolutions,
in ACL 2019.
(Download)
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Bang Liu*, Weidong Guo*, Di Niu, Chaoyue Wang, Shunnan Xu, Jinghong Lin, Kunfeng Lai and Yu Xu. (2019).
A User-Centered Concept Mining System for Query and Document Understanding at Tencent,
in KDD 2019.
(Download)
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Bang Liu, Mingjun Zhao, Di Niu, Kunfeng Lai, Yancheng He, Haojie Wei and Yu Xu. (2019).
Learning to Generate Questions by Learning What not to Generate,
in WWW 2019.
(Download)
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Ting Zhang, Bang Liu, Di Niu, Kunfeng Lai and Yu Xu. (2018).
Multiresolution Graph Attention Networks for Relevance Matching,
in CIKM 2018.
(Download)
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Bang Liu, Ting Zhang, Fred X. Han, Di Niu, Kunfeng Lai and Yu Xu. (2018).
Matching Natural Language Sentences with Hierarchical Sentence Factorization,
in WWW 2018.
(Download)
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Bang Liu, Di Niu, Kunfeng Lai, Linglong Kong, Yu Xu. (2017).
Growing Story Forest Online from Massive Breaking News,
in CIKM 2017.
This project won the Extraordinary Achievement Award, top 1 out of 18 grant holders, for 2016-2017 CCF-Tencent Rhino Bird Open Grant, on CNCC, Oct 27, Fuzhou China.
(Download)
Workshops
- Deep Learning on Graphs for Natural Language Processing (DLG4NLP@NAACL22) (Link)
Program Co-Chairs: Lingfei Wu, Bang Liu, Rada Mihalcea, Jian Pei, Yunyao Li, Yue Zhang
Location and Time: Hybrid: Seattle, Washington and Online, July 15, 2022
- Deep Learning on Graphs for Natural Language Processing (DLG4NLP@ICLR22) (Link)
Program Co-Chairs: Lingfei Wu, Bang Liu, Rada Mihalcea, Jian Pei, Yunyao Li, Yue Zhang
Location and Time: Online, April 29, 2022
Tutorials
- Deep Learning on Graphs for Natural Language Processing (DLG4NLP@NAACL22) (Link)
Presenters: Lingfei Wu, Yu Chen, Heng Ji,
Bang Liu
Conferences: WWW 2022, AAAI 2022, IJCAI 2021, SIGIR 2021, KDD 2021.