Chenwei Zhang

Chenwei Zhang

I am an Applied Scientist at Amazon, working in the Product Graph team on Information Extraction, Text Mining, and Product Categorization/Taxonomy Enrichments with special foci on a Low Resource, Open-World setting. I finished my PhD at University of Illinois at Chicago, advised by Philip S. Yu. My research interests lie in the fields of data mining and natural language processing. In particular, I am interested in text mining and mining structured information from heterogeneous information sources.

Contact: cwzhang910 AT gmail D0T com
Google Scholar | ResearchGate | LinkedIn




Research Topics:

◦  Natural Language Processing

◦  Text/Graph Mining

◦  Knowledge Graph

  NEWS

[2021/11] Accepted the invitation to serve on the Program Committee of SIGKDD 2022 Applied Data Science Track.

[2021/09] Accepted the invitation to serve on the Program Committee of TheWebConf 2022.

[2021/08] Three papers (2xRelation Extraction, Conversational Search) are accepted by EMNLP 2021.

[2021/08] Accepted the invitation to serve on the Program Committee of AAAI 2022.

[2021/08] Accepted the invitation to serve on the Program Committee of WSDM 2022.

See all

  EDUCATION

[2014/08 - 2019/05] Ph.D. in Computer Science, University of Illinois at Chicago, 2019. Advisor: Prof. Philip S. Yu

[2010/09 - 2014/05] B.Eng in Computer Science and Technology, Southwest University, China, 2014.

   WORK EXPERIENCES

[2019/08 -    Now    ] Applied Scientist at Amazon, Seattle, WA

[2017/08 - 2019/05] Research Assistant at UIC Big Data and Social Computing Lab, Chicago, IL

[2018/05 - 2018/08] Research Intern at Tencent Medical AI Lab, Palo Alto, CA

[2017/05 - 2017/08] Research Intern at Baidu Research Big Data Lab, Sunnyvale, CA

[2016/05 - 2016/07] Research Intern at Baidu Research Big Data Lab, Sunnyvale, CA

[2015/05 - 2015/08] Research Intern at Baidu Research Big Data Lab, Sunnyvale, CA

   SELECTED PUBLICATIONS

Also see the full list, and my CV.

  1. EMNLP
    Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction Xuming Hu, Chenwei Zhang, Yawen Yang, Xiaohe Li, Li Lin, Lijie Wen, and Philip S. Yu In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing 2021 [Abstract] [BibTex] [Code]
    Low-resource Relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce. Existing works either utilize self-training scheme to generate pseudo labels that will cause the gradual drift problem, or leverage meta-learning scheme which does not solicit feed-back explicitly. To alleviate selection bias due to the lack of feedback loops in existing LRE learning paradigms, we developed a Gradient Imitation Reinforcement Learning method to encourage pseudo label data to imitate the gradient descent direction on labeled data and bootstrap its optimization capability through trial and error. We also propose a framework called GradLRE, which handles two major scenarios in low-resource relation extraction. Besides the scenario where unlabeled data is sufficient, GradLRE handles the situation where no unlabeled data is available, by exploiting a contextualized augmentation method to generate data. Experimental results on two public datasets demonstrate the effectiveness of GradLRE on low resource relation extraction when comparing with baselines.
    @inproceedings{hu2021gradient,
      abbr = {EMNLP},
      topic = {NLP},
      selected = {1},
      title = {Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction},
      author = {Hu, Xuming and Zhang, Chenwei and Yang, Yawen and Li, Xiaohe and Lin, Li and Wen, Lijie and Yu, Philip S.},
      booktitle = {Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing},
      year = {2021},
      pdf = {https://arxiv.org/pdf/2109.06415.pdf},
      code = {https://github.com/THU-BPM/GradLRE}
    }
    
  2. KDD
    All You Need to Know to Build a Product Knowledge Graph Nasser Zalmout, Chenwei Zhang, Xian Li, Yan Liang, and Xin Luna Dong In Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2021 [Abstract] [BibTex] [Media]
    We answer the following key questions in this tutorial: What are unique challenges to build a product knowledge graph and what are solutions? Are these techniques applicable to building other domain knowledge graphs? What are practical tips to make this to production?
    @inproceedings{zalmout2021all,
      abbr = {KDD},
      topic = {Knowledge Graph},
      selected = {1},
      title = {All You Need to Know to Build a Product Knowledge Graph},
      author = {Zalmout, Nasser and Zhang, Chenwei and Li, Xian and Liang, Yan and Dong, Xin Luna},
      booktitle = {Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
      year = {2021},
      pdf = {https://naixlee.github.io/Product_Knowledge_Graph_Tutorial_KDD2021/},
      media = {https://naixlee.github.io/Product_Knowledge_Graph_Tutorial_KDD2021/}
    }
    
  3. TheWebConf
    Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks Xinyang Zhang, Chenwei Zhang, Xin Luna Dong, Jingbo Shang, and Jiawei Han In Proceedings of the Web Conference 2021 [Abstract] [BibTex] [Code] [Video]
    Text categorization is an essential task in Web content analysis. Considering the ever-evolving Web data and new emerging categories, instead of the laborious supervised setting, in this paper, we focus on the minimally-supervised setting that aims to categorize documents effectively, with a couple of seed documents annotated per category. We recognize that texts collected from the Web are often structure-rich, i.e., accompanied by various metadata. One can easily organize the corpus into a text-rich network, joining raw text documents with document attributes, high-quality phrases, label surface names as nodes, and their associations as edges. Such a network provides a holistic view of the corpus’ heterogeneous data sources and enables a joint optimization for network-based analysis and deep textual model training. We therefore propose a novel framework for minimally supervised categorization by learning from the text-rich network. Specifically, we jointly train two modules with different inductive biases – a text analysis module for text understanding and a network learning module for class-discriminative, scalable network learning. Each module generates pseudo training labels from the unlabeled document set, and both modules mutually enhance each other by co-training using pooled pseudo labels. We test our model on two real-world datasets. On the challenging e-commerce product categorization dataset with 683 categories, our experiments show that given only three seed documents per category, our framework can achieve an accuracy of about 92%, significantly outperforming all compared methods; our accuracy is only less than 2% away from the supervised BERT model trained on about 50K labeled documents.
    @inproceedings{zhang2021minimally,
      abbr = {TheWebConf},
      topic = {Graph Mining},
      selected = {1},
      title = {Minimally-Supervised Structure-Rich Text Categorization via Learning on Text-Rich Networks},
      author = {Zhang, Xinyang and Zhang, Chenwei and Dong, Xin Luna and Shang, Jingbo and Han, Jiawei},
      booktitle = {Proceedings of the Web Conference},
      year = {2021},
      pdf = {https://arxiv.org/pdf/2102.11479.pdf},
      code = {https://github.com/xinyangz/ltrn},
      video = {https://videolectures.net/www2021_zhang_minimally_supervised/}
    }
    
  4. EMNLP
    SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction Xuming Hu, Chenwei Zhang, Yusong Xu, Lijie Wen, and Philip S. Yu In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing 2020 [Abstract] [BibTex] [Code]
    Open relation extraction is the task of extracting open-domain relation facts from natural language sentences. Existing works either utilize heuristics or distant-supervised annotations to train a supervised classifier over pre-defined relations, or adopt unsupervised methods with additional assumptions that have less discriminative power. In this work, we propose a self-supervised framework named SelfORE, which exploits weak, self-supervised signals by leveraging large pretrained language model for adaptive clustering on contextualized relational features, and bootstraps the self-supervised signals by improving contextualized features in relation classification. Experimental results on three datasets show the effectiveness and robustness of SelfORE on open-domain Relation Extraction when comparing with competitive baselines.
    @inproceedings{hu2020selfore,
      abbr = {EMNLP},
      topic = {NLP},
      selected = {1},
      title = {SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction},
      author = {Hu, Xuming and Zhang, Chenwei and Xu, Yusong and Wen, Lijie and Yu, Philip S.},
      booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing},
      year = {2020},
      pdf = {https://arxiv.org/pdf/2004.02438.pdf},
      code = {https://github.com/THU-BPM/SelfORE}
    }
    
  5. KDD
    AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types Xin Luna Dong, Xiang He, Andrey Kan, Xian Li, Yan Liang, Jun Ma, Yifan Ethan Xu, Chenwei Zhang, Tong Zhao, Gabriel Blanco Saldana, and others In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2020 [Abstract] [BibTex] [Media]
    Can one build a knowledge graph (KG) for all products in the world? Knowledge graphs have firmly established themselves as valuable sources of information for search and question answering, and it is natural to wonder if a KG can contain information about products offered at online retail sites. There have been several successful examples of generic KGs, but organizing information about products poses many additional challenges, including sparsity and noise of structured data for products, complexity of the domain with millions of product types and thousands of attributes, heterogeneity across large number of categories, as well as large and constantly growing number of products. We describe AutoKnow, our automatic (self-driving) system that addresses these challenges. The system includes a suite of novel techniques for taxonomy construction, product property identification, knowledge extraction, anomaly detection, and synonym discovery. AutoKnow is (a) automatic, requiring little human intervention, (b) multi-scalable, scalable in multiple dimensions (many domains, many products, and many attributes), and (c) integrative, exploiting rich customer behavior logs. AutoKnow has been operational in collecting product knowledge for over 11K product types.
    @inproceedings{dong2020autoknow,
      abbr = {KDD},
      topic = {Knowledge Graph},
      selected = {1},
      title = {AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types},
      author = {Dong, Xin Luna and He, Xiang and Kan, Andrey and Li, Xian and Liang, Yan and Ma, Jun and Xu, Yifan Ethan and Zhang, Chenwei and Zhao, Tong and Blanco Saldana, Gabriel and others},
      booktitle = {Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
      pages = {2724--2734},
      year = {2020},
      pdf = {https://arxiv.org/pdf/2006.13473.pdf},
      media = {https://www.amazon.science/blog/building-product-graphs-automatically}
    }
    
  6. ACL
    Joint Slot Filling and Intent Detection via Capsule Neural Networks Chenwei Zhang, Yaliang Li, Nan Du, Wei Fan, and Philip S. Yu In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics 2019 [BibTex] [Poster] [Code]
    @inproceedings{zhang2019joint,
      abbr = {ACL},
      topic = {NLP},
      selected = {1},
      title = {Joint Slot Filling and Intent Detection via Capsule Neural Networks},
      author = {Zhang, Chenwei and Li, Yaliang and Du, Nan and Fan, Wei and Yu, Philip S.},
      booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
      pages = {5259--5267},
      year = {2019},
      pdf = {https://arxiv.org/pdf/1812.09471.pdf},
      poster = {https://drive.google.com/file/d/1rZpP-4WY7T8AtARXde7qZd5enV53yNOL/view},
      code = {https://github.com/czhang99/Capsule-NLU}
    }
    
  7. EMNLP
    Zero-shot User Intent Detection via Capsule Neural Networks Congying Xia*, Chenwei Zhang*, Xiaohui Yan, Yi Chang, and Philip S. Yu In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing 2018 [BibTex] [Code] [Video]
    @inproceedings{xia2018zero,
      abbr = {EMNLP},
      topic = {NLP},
      selected = {1},
      title = {Zero-shot User Intent Detection via Capsule Neural Networks},
      author = {Xia*, Congying and Zhang*, Chenwei and Yan, Xiaohui and Chang, Yi and Yu, Philip S.},
      booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
      pages = {3090--3099},
      year = {2018},
      pdf = {https://arxiv.org/pdf/1809.00385.pdf},
      video = {https://vimeo.com/305945714},
      code = {https://github.com/congyingxia/ZeroShotCapsule}
    }
    


   PROFESSIONAL SERVICES

Program Committee: ACL, EMNLP, NAACL, KDD, AAAI, WSDM, TheWebConf, RecSys, CIKM, AACL

Reviewer: TKDE, TKDD, VLDB, NeuroComputing, TBD, TOIS, KDD, WSDM, ICDM, PAKDD, ICWSM, ASONAM

Organzing Committee: KR2ML@NeurIPS 2020