Chao Zhang
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Assistant Professor School of Computational Science and Engineering College of Computing Georgia Institute of Technology Office: CODA 1309 Address: 756 W Peachtree St NW, Atlanta, GA Email: chaozhang@gatech.edu |
Welcome to my homepage! I am an Assistant Professor at the School of Computational Science and Engineering at Georgia Tech. Before joining Georgia Tech, I received my Ph.D. in Computer Science from UIUC in 2018, where I worked with Prof. Jiawei Han.
Research
I am broadly interested in developing intelligent systems that can facilitate data-driven task support and decision making. I am recently focusing on applying machine learning to analyze massive data (text, graphs, spatiotemporal data) to accelerate scientific discovery, especially for material science and biomedical applications. Below are some research problems I am working on:
- Knowledge extraction and text mining: Extract information about entities, relations, taxonomies, and events from unstructured data such as Web data and scientific literature. We are tackling challenges for low-resource information extraction and multi-modal information extraction:
- BOND: Bert-Assisted Open-Domain Named Entity Recognition with Distant Supervision, KDD 2020
- STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths, KDD 2020
- TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering, KDD 2018
- TrioVecEvent: Embedding-Based Online Local Event Detection in Geo-Tagged Tweet Streams, KDD 2017
- GeoBurst: Real-Time Local Event Detection in Geo-Tagged Tweet Streams, SIGIR 2016
- Learning predictive models from limited data: The lack of labeled training data is a major bottleneck for training deep predictive models. We learn deep learning predictive models from a small amount of labeled data, or just using weakly labeled data derived from domain knowledge. Our recent works combine self-supervised learning with self-training as a generic approach to learning from limited supervision.
- Text Classification Using Label Names Only: A Language Model Self-Training Approach, EMNLP 2020
- Denoising Multi-Source Weak Supervision for Neural Text Classification, EMNLP 2020
- SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup, EMNLP 2020
- Weakly-Supervised Neural Text Classification, CIKM 2018
- Uncertainty estimation and out-of-distribution robustness: Current deep learning models typically assume the training and test distributions are i.i.d., which is rarely the case in practice. As a result, deep learning models can fail miserably in open-world settings. We want machines to go beyond the training distribution: How can we quantify uncertainties for deep learning models and enable them say "I don't know" for out-of-distribution samples? How can we make them to generalize to different domains and make robust predictions? How can we make them fast adapt to tasks that were never seen before?
- Guided Generative Models: We study how to guide deep generative models with pre-conditions, to generate samples that satisfy desired properties.
- Spatiotemporal data mining: I have also worked on spatiotemporal data mining, along two lines: 1) Deep learning for predictive spatiotemporal data analytics; 2) Multi-modal spatiotemporal analytics for semantics-rich spatiotemporal understanding:
- Computing Trajectory Similarity in Linear Time: A Generic Seed-Guided Neural Metric Learning Approach, ICDE 2019
- DeepMove: Predicting Human Mobility with Attentional Recurrent Networks, WWW 2018
- Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning, WWW 2017
- GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media, KDD 2016
News
- We have 4 paper accepted by EMNLP'20, discussing model calibration, sequence labeling, and zero-shot text classification.
- We have 1 paper accepted by ICML'20. We introduce a new method for quantifying uncertainties for neural networks based on the connection between NN and linear systems.
- We have 4 papers accepted by KDD'20, discussing self-supervised taxonomy construction, low-resource NER, hierarchical topic mining, and tensor factorization.
- Congrats to my student Wendi Ren for winning the Marshall D. Williamson Fellowship!
- Honored to receive the 2020 Google Faculty Research Award!
- Two papers accepted by the Web Conference 2020.
- Honored to receive the ACM SIGKDD 2019 Dissertation Runner-up Award!
- Our monograph Multidimensional Mining of Massive Text Data is published by Morgan & Claypool!
Awards
- 2020 Amazon AWS Machine Learning Research Award
- 2020 Google Faculty Research Award
- 2019 ACM SIGKDD Dissertation Award Runner-up
- 2018 ACM IMWUT Distinguished Paper Award
- 2015 ECML/PKDD Best Student Paper Runner-up Award
- 2013 Chiang Chen Overseas Graduate Fellowship
Publications
(* denotes equal contribution)
2020
- Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data
Lingkai Kong, Haoming Jiang, Yuchen Zhuang, Jie Lyu, Tuo Zhao and Chao Zhang.
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020 - Text Classification Using Label Names Only: A Language Model Self-Training Approach
Yu Meng, Yunyi Zhang, Jiaxin Huang, Chenyan Xiong, Heng Ji, Chao Zhang, Jiawei Han.
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020 - SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup
Rongzhi Zhang, Yue Yu and Chao Zhang.
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020 - Denoising Multi-Source Weak Supervision for Neural Text Classification
Wendi Ren, Yinghao Li, Hanting Su, David Kartchner, Cassie Mitchell, and Chao Zhang.
Findings of Conference on Empirical Methods in Natural Language Processing (EMNLP-Findings), 2020 - SDE-Net: Equipping Deep Neural Networks with Uncertainty Estimates
Lingkai Kong, Jimeng Sun, Chao Zhang.
International Conference on Machine Learning (ICML), 2020 - STEAM: Self-Supervised Taxonomy Expansion with Mini-Paths
Yue Yu, Yinghao Li, Jiaming Shen, Hao Feng, Jimeng Sun and Chao Zhang.
ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2020 - BOND: Bert-Assisted Open-Domain Named Entity Recognition with Distant Supervision
Chen Liang*, Yue Yu*, Haoming Jiang, Siawpeng Er, Ruijia Wang, Tuo Zhao and Chao Zhang
ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2020 - LogPar: Logistic PARAFAC2 Factorization for Temporal Binary Data with Missing Values
Kejing Yin, Ardavan Afshar, Joyce Ho, William Cheung, Chao Zhang and Jimeng Sun
ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2020 - Hierarchical Topic Mining via Joint Spherical Tree and Text Embedding
Yu Meng, Yunyi Zhang, Jiaxin Huang, Yu Zhang, Chao Zhang and Jiawei Han
ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2020 - Joint Aspect-Sentiment Analysis with Minimal User Guidance
Honglei Zhuang, Fang Guo, Chao Zhang, Liyuan Liu and Jiawei Han.
ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2020. - Discriminative Topic Mining via Category-Name Guided Text Embedding
Yu Meng, Jiaxin Huang, Guangyuan Wang, Zihan Wang, Chao Zhang, Yu Zhang and Jiawei Han.
The Web Conference (WWW), 2020 - paper2repo: GitHub Repository Recommendation for Academic Papers
Huajie Shao, Dachun Sun, Jiahao Wu, Zecheng Zhang, Aston Zhang, Shuochao Yao, Shengzhong Liu, Tianshi Wang, Chao Zhang and Tarek Abdelzaher.
The Web Conference (WWW), 2020
2019
- Multidimensional Mining of Massive Text Data
Chao Zhang, Jiawei Han.
Morgan & Claypool Publishers, 2019 - Spherical Text Embedding
Yu Meng, Jiaxin Huang, Guangyuan Wang, Chao Zhang, Honglei Zhuang, Lance Kaplan, Jiawei Han.
Annual Conference on Neural Information Processing Systems (NeurIPS), 2019 - State-Sharing Sparse Hidden Markov Models for Personalized Sequences
Hongzhi Shi, Chao Zhang, Mingquan Yao, Yong Li, Funing Sun, Depeng Jin.
ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2019 - TopicMine: User-Guided Topic Mining by Category-Oriented Embedding
Yu Meng, Jiaxin Huang, Zihan Wang, Chenyu Fan, Guangyuan Wang, Chao Zhang, Jingbo Shang, Lance Kaplan, Jiawei Han.
ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2019
(Demo) - CubeNet: Multi-Facet Hierarchical Heterogeneous Network Construction, Analysis, and Mining
Carl Yang, Dai Teng, Siyang Liu, Sayantani Basu, Jieyu Zhang, Jiaming Shen, Chao Zhang, Jingbo Shang, Lance Kaplan, Timothy Harratty, and Jiawei Han.
ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2019
(Demo) - A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization
Yucheng Chen, Matus Telgarsky, Chao Zhang, Bolton Bailey, Daniel Hsu, Jian Peng.
International Conference on Machine Learning (ICML), 2019 - Weakly-Supervised Hierarchical Text Classification
Yu Meng, Jiaming Shen, Chao Zhang, Jiawei Han.
AAAI Conference on Artificial Intelligence (AAAI), 2019 - Computing Trajectory Similarity in Linear Time: A Generic Seed-Guided Neural Metric Learning Approach
Di Yao, Gao Cong, Chao Zhang, Jingping Bi.
IEEE International Conference on Data Engineering (ICDE), 2019 - DPLink: User Identity Linkage via Deep Neural Network From Heterogeneous Mobility Data
Jie Feng, Mingyang Zhang, Huandong Wang, Zeyu Yang, Chao Zhang, Yong Li, Depeng Jin.
The Web Conference (WWW), 2019 - GeoAttn: Localization of Social Media Messages Via Attentional Memory Network
Sha Li, Chao Zhang, Dongming Lei, Ji Li, Jiawei Han.
SIAM International Conference on Data Mining (SDM), 2019 - Semantics-Aware Hidden Markov Model for Human Mobility
Hongzhi Shi, Hancheng Cao, Xiangxin Zhou, Yong Li, Chao Zhang, Vassilis Kostakos, Funing Sun, Fanchao Meng.
SIAM International Conference on Data Mining (SDM), 2019
2018
- Multi-Dimensional Mining of Unstructured Data with Limited Supervision
Chao Zhang
Ph.D. Thesis
(ACM SIGKDD 2019 Dissertation Runner-up Award) - TaxoGen: Unsupervised Topic Taxonomy Construction by Adaptive Term Embedding and Clustering
Chao Zhang, Fangbo Tao, Xiusi Chen, Jiaming Shen, Meng Jiang, Brian Sadler, Michelle Vanni, Jiawei Han.
ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2018
(Code) (Data) - HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion
Jiaming Shen, Zeqiu Wu, Dongming Lei, Chao Zhang, Xiang Ren, Michelle T. Vanni, Brian M. Sadler, Jiawei Han.
ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2018 - Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
Yu Shi, Qi Zhu, Fang Guo, Chao Zhang, Jiawei Han.
ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2018 - Towards Multidimensional Analysis of Text Corpora
Jingbo Shang, Chao Zhang, Jiaming Shen, Jiawei Han.
ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2018
(Tutorial) - DeepMove: Predicting Human Mobility with Attentional Recurrent Networks
Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, Depeng Jin.
The International World Wide Web Conference (WWW), 2018
(Code & Data) - Weakly-Supervised Neural Text Classification
Yu Meng, Jiaming Shen, Chao Zhang, Jiawei Han.
ACM International Conference on Information and Knowledge Management (CIKM), 2018
(Code) - Open-Schema Event Profiling for Massive News Corpora
Quan Yuan, Xiang Ren, Wenqi He, Chao Zhang, Xinhe Geng, Lifu Huang, Heng Ji, Chin-Yew Lin, Jiawei Han.
ACM International Conference on Information and Knowledge Management (CIKM), 2018 - Spatiotemporal Activity Modeling Under Data Scarcity: A Graph-Regularized Cross-Modal Embedding Approach
Chao Zhang, Mengxiong Liu, Zhengchao Liu, Carl Yang, Luming Zhang, and Jiawei Han.
AAAI Conference on Artificial Intelligence (AAAI), 2018 - A Spherical Hidden Markov Model for Semantics-Rich Human Mobility Modeling
Wanzheng Zhu +, Chao Zhang +, Shuochao Yao, Xiaobin Gao, and Jiawei Han.
AAAI Conference on Artificial Intelligence (AAAI), 2018 - Doc2Cube: Allocating Documents to Text Cube without Labeled Data
Fangbo Tao +, Chao Zhang +, Xiusi Chen, Meng Jiang, Tim Hanratty, Lance Kaplan, Jiawei Han.
IEEE International Conference on Data Mining (ICDM), 2018
(Code) - RDeepSense: Reliable Deep Mobile Computing Models with Uncertainty Estimations
Shuochao Yao, Yiran Zhao, Huajie Shao, Aston Zhang, Chao Zhang, Shen Li, and Tarek Abdelzaher.
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2018 - SenseGAN: Enabling Deep Learning for Internet of Things with a Semi-Supervised Framework
Shuochao Yao, Yiran Zhao, Huajie Shao, Chao Zhang, Aston Zhang, Shaohan Hu, Dongxin Liu, Shengzhong Liu, and Tarek Abdelzaher.
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 2018
(Distinguished Paper Award) - Deep Learning for the Internet of Things
Shuochao Yao, Yiran Zhao, Aston Zhang, Huajie Shao, Chao Zhang, Lu Su, Tarek Abdelzaher.
IEEE Computer, 2018 - GeoBurst+: Effective and Real-Time Local Event Detection in Geo-Tagged Tweet Streams
Chao Zhang, Dongming Lei, Quan Yuan, Honglei Zhuang, Lance Kaplan, Shaowen Wang, Jiawei Han.
ACM Transactions on Intelligent Systems and Technology (TIST), 2018 - Leveraging the Power of Informative Users for Local Event Detection
Hengtong Zhang, Fenglong Ma, Yaliang Li, Chao Zhang, Tianqi Wang, Yaqing Wang, Jing Gao, Lu Su.
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2018 - Learning deep representation for trajectory clustering
Di Yao, Chao Zhang, Zhihua Zhu, Qin Hu,heng Wang, Jianhui Huang, Jingping Bi.
Expert Systems, 2018. - Did You Enjoy the Ride: Understanding Passenger Experience via Heterogeneous Network Embedding
Carl Yang, Chao Zhang, Jiawei Han, Xuewen Chen, and Jieping Ye.
IEEE International Conference on Data Engineering (ICDE), 2018 - ApDeepSense: Deep Learning Uncertainty Estimation without the Pain for IoT Applications
Shuochao Yao, Yiran Zhao, Huajie Shao, Chao Zhang, Aston Zhang, Dongxin Liu, Shengzhong Liu, Lu Su, Tarek Abdelzaher.
IEEE International Conference on Distributed Computing Systems (ICDCS), 2018 - A Constrained Maximum Likelihood Estimator for Unguided Social Sensing
Huajie Shao, Shuochao Yao, Yiran Zhao, Chao Zhang, Jinda Han, Lance Kaplan, Su Lu, and Tarek Abdelzaher.
IEEE International Conference on Computer Communications (InfoCom), 2018 - Towards Personalized Activity Level Prediction in Community Question Answering Websites
Zhenguang Liu, Yingjie Xia, Qi Liu, Qinming He, Yanxiang Chen, Chao Zhang, and Roger Zimmermann.
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2018
2017
- TrioVecEvent: Embedding-Based Online Local Event Detection in Geo-Tagged Tweet Streams
Chao Zhang, Liyuan Liu, Dongming Lei, Quan Yuan, Honglei Zhuang, Tim Hanratty, and Jiawei Han.
ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2017
(Slides) (Code) (Video) (Featured by Illinois Innovator) - Bridging Collaborative Filtering and Semi-Supervised Learning: A Neural Approach for POI Recommendation
Carl Yang, Lanxiao Bai, Chao Zhang, Quan Yuan and Jiawei Han.
ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2017.
(Code & Data) - ReAct: Online Multimodal Embedding for Recency-Aware Spatiotemporal Activity Modeling
Chao Zhang, Keyang Zhang, Quan Yuan, Fangbo Tao, Luming Zhang, Tim Hanratty, and Jiawei Han.
ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2017
(Slides) (Code) (Data) - Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning
Chao Zhang, Keyang Zhang, Quan Yuan, Haoruo Peng, Yu Zheng, Tim Hanratty, Shaowen Wang, and Jiawei Han.
International World Wide Web Conference (WWW), 2017
(Slides) (Code & Data) - Bringing Semantics to Spatiotemporal Data Mining: Challenges, Methods, and Applications
Chao Zhang, Quan Yuan, and Jiawei Han.
IEEE International Conference on Data Engineering (ICDE), 2017
(Tutorial) - PRED: Periodic Region Detection for Mobility Modeling of Social Media Users
Quan Yuan, Wei Zhang, Chao Zhang, Xinhe Geng, Gao Cong, and Jiawei Han.
ACM International Conference on Web Search and Data Mining (WSDM), 2017
(Code & Data) - Towards Space and Time Coupled Social Media Analysis
Chao Zhang, Quan Yuan, Shi Zhi, Sha Li, and Jiawei Han.
2017 ACM International Conference on Information and Knowledge Management (CIKM), 2017
(Tutorial) - Detecting Multiple Periods and Periodic Patterns in Event Time Sequences
Quan Yuan, Jingbo Shang, Xin Cao, Chao Zhang, Xinhe Geng, Jiawei Han.
ACM International Conference on Information and Knowledge Management (CIKM), 2017 - SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories
Di Yao, Chao Zhang, Jianhui Huang, and Jingping Bi
ACM International Conference on Information and Knowledge Management (CIKM), 2017
(Code & Data) - Urbanity: A System for Interactive Exploration of Urban Dynamics from Streaming Human Sensing Data
Mengxiong Liu, Zhengchao Liu, Chao Zhang, Keyang Zhang, Quan Yuan, Tim Hanratty, and Jiawei Han
ACM International Conference on Information and Knowledge Management (CIKM), 2017
(Demo) - ClaimVerif: A Real-time Claim Verification System Using the Web and Fact Databases
Shi Zhi, Yicheng Sun, Jiayi Liu, Chao Zhang, and Jiawei Han.
ACM International Conference on Information and Knowledge Management (CIKM), 2017 - Trajectory Clustering via Deep Representation Learning
Di Yao, Chao Zhang, Zhihua Zhu, Jianhui Huang, and Jingping Bi.
International Joint Conference on Neural Networks (IJCNN), 2017
(Code) - pg-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data
Julie Yixuan Zhu +, Chao Zhang +, Huichu Zhang, Shi Zhi, Victor O.K. Li, Jiawei Han, and Yu Zheng.
IEEE Transactions on Big Data (TBD), 2017 - Geographical Data Mining
Chao Zhang and Jiawei Han.
The International Encyclopedia of Geography: People, the Earth, Environment and Technology, 2017 - A Survey on Spatiotemporal and Semantic Data Mining
Quan Yuan, Chao Zhang, Jiawei Han.
Trends in Spatial Analysis and Modelling, Springer, 2017
2016
- GMove: Group-Level Mobility Modeling Using Geo-Tagged Social Media
Chao Zhang, Keyang Zhang, Quan Yuan, Luming Zhang, Tim Hanratty, and Jiawei Han.
ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2016
(Slides) (Code & Data)
- GeoBurst: Real-Time Local Event Detection in Geo-Tagged Tweet Streams
Chao Zhang, Guangyu Zhou, Quan Yuan, Honglei Zhuang, Yu Zheng, Lance Kaplan, Shaowen Wang, Jiawei Han.
ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2016
(Slides) (Code & Data)
- Mining Contiguous Sequential Generators in Biological Sequences
Jingsong Zhang, Yinglin Wang, Chao Zhang, and Yongyong Shi
Transactions on Computational Biology and Bioinformatics (TCBB), 13(5): 855–867, 2016
2015
- Assembler: Efficient Discovery of Spatial Co-evolving Patterns in Massive Geo-sensory Data
Chao Zhang, Yu Zheng, Xiuli Ma, Jiawei Han.
ACM SIGKDD Conference on Knowledge Discovery and Pattern Mining (KDD), 2015
- Fast Inbound Top-K Query for Random Walk with Restart
Chao Zhang, Shan Jiang, Yucheng Chen, Yidan Sun, Jiawei Han.
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2015
(Best Student Paper Runner-up Award) - StreamCube: Hierarchical Spatio-temporal Hashtag Clustering for Event Exploration over the Twitter Stream
Wei Feng, Chao Zhang, Wei Zhang, Jiawei Han, Jianyong Wang, Charu Aggarwal, Jianbin Huang.
IEEE International Conference on Data Engineering (ICDE), 2015
2014
- Splitter: Mining Fine-Grained Sequential Patterns in Semantic Trajectories
Chao Zhang, Jiawei Han, Lidan Shou, Jiajun Lu, Thomas La Porta.
International Conference on Very Large Data Bases (VLDB), 2014
(Slides) (Code & Data) - Trendspedia: An Internet Observatory for Analyzing and Visualizing the Evolving Web
Wei Kang, Anthony K. H. Tung, Wei Chen, Xinyu Li, Qiyue Song, Chao Zhang, Feng Zhao, Xiajuan Zhou.
IEEE International Conference on Data Engineering (ICDE), 2014
Earlier
- Supporting Pattern-Preserving Anonymization for Time-Series Data
Lidan Shou, Xuan Shang, Ke Chen, Gang Chen, Chao Zhang.
IEEE Transactions on Knowledge and Data Engineering (TKDE), 25(4): 877-892, 2013 - Evaluating Geo-Social Influence in Location-Based Social Networks
Chao Zhang, Lidan Shou, Ke Chen, Gang Chen, Yijun Bei.
ACM International Conference on Information and Knowledge Management (CIKM), 2012 - See-To-Retrieve: Efficient Processing of Spatio-Visual Keyword Queries
Chao Zhang, Lidan Shou, Ke Chen, Gang Chen.
ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2012 - What-You-Retrieve-Is-What-You-See: A Preliminary Cyber-Physical Search Engine
Lidan Shou, Ke Chen, Gang Chen, Chao Zhang, Yi Ma, Xian Zhang.
ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2011
Software
- SDE-Net: Efficient uncertainty estimation for deep neural networks
- BOND: Distantly-supervised named entity recognition
- STEAM: Automatic taxonomy expansion
- TaxoGen: Unsupervised topic taxonomy construction from text corpus
- WestClass: Weakly-supervised text classification
- GeoBurst: Unsupervised spatiotemporal event detection from social media streams