Natural language processing

  1. Eric Graves, Qiang Ning, and Prithwish Basu. “An information theoretic model for summarization, and some basic results.” IEEE International Symposium on Information Theory (ISIT), 2019. [arxiv version] [slides]
    • Understanding text summarization from a perspective of information theory.
  2. Qiang Ning, Hangfeng He, Chuchu Fan, and Dan Roth. “Partial or Complete, That’s The Question.” NAACL, 2019. [website] [pdf]
  3. Qiang Ning, Ben Zhou, Zhili Feng, Haoruo Peng, and Dan Roth. “CogCompTime: A Tool for Understanding Time in Natural Language Text.” EMNLP (demo track), 2018. [website] [pdf] [poster] [online demo][github].
    • A state-of-the-art automatic tool for:
    • Time expression extraction and normalization
    • Temporal relation extraction
  4. Qiang Ning, Hao Wu, and Dan Roth. “A Multi-Axis Annotation Scheme for Event Temporal Relations.” ACL, 2018. [website] [pdf] [Annotation Guidelines] [talk] [github:MATRES]
    • A new crowdsourcing annotation scheme for collecting temporal relation data more reliably and more efficiently
    • Achieved approx. 20% improvement in performance
  5. Qiang Ning, Zhili Feng, Hao Wu, and Dan Roth. “Joint Reasoning for Temporal and Causal Relations.” ACL, 2018. [website][pdf] [talk] [github:TCR]
  6. Qiang Ning, Zhongzhi Yu, Chuchu Fan, and Dan Roth. “Exploiting Partially Annotated Data in Temporal Relation Extraction.” *SEM, 2018 (short paper). [website][pdf] [poster] [github]
    • Incidental supervision for temporal relation extraction
  7. Qiang Ning, Hao Wu, Haoruo Peng, and Dan Roth. “Improving Temporal Relation Extraction with a Globally Acquired Statistical Resource.” NAACL, 2018. [website][pdf] [poster] [github:TemProb] [Download TemProb]
    • Knowledge-base encoding prior statistics
    • For example, “die” should be after “explode”, instead of before; “ask” should be before “help” instead of after
    • Mined from a million NYT news articles using Amazon Web Services (AWS)
  8. Qiang Ning, Zhili Feng, and Dan Roth. “A Structured Learning Approach to Temporal Relation Extraction.” EMNLP, 2017. [website][pdf] [talk] [github]
    • Structured Learning
    • Constraint-Driven Learning (CoDL)

Signal processing

  1. Qiang Ning, Chao Ma, Fan Lam, and Zhi-Pei Liang. “Spectral Quantification for High-Resolution MR Spectroscopic Imaging with Spatiospectral Constraints.” IEEE Transactions on Biomedical Engineering, vol. 64, no. 5, p1178-1186, May 2017 (DOI: 10.1109/TBME.2016.2594583). [pdf]
    • Brain anatomy guided metabolite measuring
    • Cramer-Rao bound analysis
    • Accelerated brain imaging
  2. Chao Ma, Fan Lam, Qiang Ning, Curtis Johnson, and Zhi-Pei Liang. “High-Resolution 1H-MRSI of the Brain Using Short-TE SPICE.” Magnetic Resonance in Medicine, vol. 77, no. 2, p467-479, Feb 2017. [pdf]
  3. Qiang Ning, Chao Ma, Fan Lam, Bryan Clifford, and Zhi-Pei Liang. “Removal of Nuisance Signal from Sparsely Sampled 1H-MRSI Data Using Physics-based Spectral Bases.” 24th Annual ISMRM Scientific Meeting and Exhibition, Singapore, Singapore, May 2016. [pdf] [unsubmitted draft]
  4. Qiang Ning, Chao Ma, and Zhi-Pei Liang. “Spectral Estimation for Magnetic Resonance Spectroscopic Imaging with Spatial Sparsity Constraints.” IEEE International Symposium on Biomedical Imaging: From Nano to Macro, New York, April 2015. [pdf]
  5. Qiang Ning, Chao Ma, and Zhi-Pei Liang. “Joint Estimation of Spectral Parameters from MR Spectroscopic Imaging Data” 23nd Annual ISMRM Scientific Meeting and Exhibition, Toronto, Canada, June 2015. [pdf]
  6. Qiang Ning, Chao Ma, Curtis Johnson, and Zhi-Pei Liang. “Towards Short-TE MR Spectroscopic Imaging: Spectral Decomposition and Removal of Baseline Signals.” 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, August 2014. [pdf] [poster]
  7. Qiang Ning, Kan Chen, Li Yi, Chuchu Fan, Yao Lu, and Jiangtao Wen. “Image Super-Resolution via Analysis Sparse Prior.” IEEE Signal Processing Letters, vol. 20, no. 4, p399-402, April 2013. [pdf] [code]
    • Image reconstruction/super-resolution
    • Compressed sensing


  1. CN2012105247162, granted April 17, 2013.

Reviewer for

  • The Annual Meeting of the Association for Computational Linguistics (ACL)
  • Conference on Empirical Methods in Natural Language Processing (EMNLP)
  • The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)
  • AAAI Conference on Artificial Intelligence
  • European Conference on Information Retrieval (ECIR)
  • Journal of Artificial Intelligence Research (JAIR)
  • IEEE Signal Processing Letters (SPL)
  • IEEE Transactions on Biomedical Engineering (TBME)
  • Magnetic Resonance Imaging (MRM)