I am a Senior Applied Scientist at Amazon AWS AI. I am working on research and production of Machine Learning and Natural Language Processing techniques to solve customer problems.
I'm broadly interested in Deep Learning and Natural Langauge Processing. I'm particularly interested in building explainable and trustworthy machine learning models for real-world problems.
Previously, I was a Senior Research Scientist at Bosch Research, where I built machine learning solutions for industrial applications such as dialogue sentiment analysis, customer service, requirement document understanding, knowledge extraction and construction etc. I obtained my PhD in Computer Science from University of Utah, where I worked on Machine Learning and Natural Language Processing with a focus on Information Extraction, Affective Event Understanding, and Sentiment Analysis.
Publications
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Weakly Supervised Named Entity Tagging with Learnable Logical Rules
Jiacheng Li, Haibo Ding, Jingbo Shang, Julian McAuley, Zhe Feng,
ACL-2021
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Explicitly Capturing Relations between Entity Mentions via Graph Neural Networks for Domain-specific Named Entity Recognition
Pei Chen, Haibo Ding, Jun Araki, Ruihong Huang
ACL-2021
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How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering
Zhengbao Jiang, Jun Araki, Haibo Ding, Graham Neubig
Transactions of the Association for Computational Linguistics (TACL) 2021
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GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition
Xinyan Zhao, Haibo Ding, and Zhe Feng, Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics (EACL-2021)
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X-FACTR: Multilingual Factual Knowledge Retrieval from Pretrained Language Models
Zhengbao Jiang, Antonios Anastasopoulos, Jun Araki, Haibo Ding, and Graham Neubig, Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP-2020).
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Learning to Classify Events from Human Needs Category Descriptions
Haibo Ding and Zhe Feng, Findings of EMNLP 2020.
(code)
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Improving Human Needs Categorization of Events with Semantic Classification
Haibo Ding, Ellen Riloff, Zhe Feng, Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)
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Human Needs Categorization of Affective Events Using Labeled and Unlabeled Data
Haibo Ding, Ellen Riloff, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL HLT 2018)
- Weakly Supervised Induction of Affective Events by Optimizing Semantic Consistency
Haibo Ding, Ellen Riloff, Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI 2018)
- Why is an Event Affective? Classifying Affective Events based on Human Needs
Haibo Ding, Tianyu Jiang, Ellen Riloff, The AAAI-18 Workshop on Affective Content Analysis (AFFCON 2018)
- Acquiring Knowledge of Affective Events from Blogs using Label Propagation
Haibo Ding, Ellen Riloff, Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016)
- Extracting Information about Medication Use from Veterinary Discussions
Haibo Ding, Ellen Riloff, Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics - Human Language Technologies (NAACL HLT 2015)
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A Reranking method for syntactic parsing with heterogeneous treebanks
Haibo Ding, Muhua Zhu, Jingbo Zhu, Natural Language Processing and Knowledge Engineering (IEEE NLP-KE 2010)
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A Multi-stage Clustering Approach to Chinese Person Name Disambiguation
Haibo Ding, Tong Xiao, Jingbo Zhu, Proceedings of the 6th China National Conference on Information Retrieval (CCIR2010)
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A Multi-stage Clustering Framework for Chinese Personal Name Disambiguation
Huizhen Wang, Haibo Ding, Yingchao Shi, Ji Ma, Xiao Zhou and Jingbo Zhu CIPS-SIGHAN Joint Conference on Chinese Language Processing (CLP2010)
Contact
hbding AT amazon DOT com