Publications

Natural language processing

  1. Hangfeng He, Mingyuan Zhang, Qiang Ning, and Dan Roth. “Foreshadowing the Benefits of Incidental Supervision.” arxiv
  2. Kaifu Wang, Qiang Ning, and Dan Roth. “Learnability with Indirect Supervision Signals.” NeurIPS, 2020. arxiv
  3. Matt Gardner and many others. “Evaluating Models’ Local Decision Boundaries via Contrast Sets.” EMNLP, 2020. arxiv
  4. Qiang Ning, Hao Wu, Rujun Han, Nanyun Peng, Matt Gardner, and Dan Roth. “TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions.” EMNLP, 2020. arxiv
  5. Qiang Ning, Hao Wu, Pradeep Dasigi, Dheeru Dua, Matt Gardner, Robert L. Logan IV, and Zhen Nie. “Easy, Reproducible and Quality-Controlled Data Collection with CROWDAQ.” EMNLP, 2020 (demo paper).
  6. Hangfeng He, Qiang Ning, and Dan Roth. “QuASE: Question-Answer Driven Sentence Encoding.” ACL, 2020. arxiv
  7. Ben Zhou, Qiang Ning, and Dan Roth. “Temporal Common Sense Acquisition with Minimal Supervision.” ACL, 2020. arxiv
  8. Haoruo Peng, Qiang Ning, and Dan Roth. “KnowSemLM: A Knowledge Infused Semantic Language Model.” CoNLL, 2019. [website] [talk] [github]
  9. Rujun Han, Qiang Ning, and Nanyun Peng. “Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction.” EMNLP, 2019. [pdf] [poster] [github]
  10. Ben Zhou, Daniel Khashabi, Qiang Ning, and Dan Roth. “‘Going on a vacation’ takes longer than ‘Going for a walk’: A Study of Temporal Commonsense Understanding.” EMNLP, 2019 (short paper). [website] [talk slides] [talk pdf] [github]
  11. Qiang Ning, Sanjay Subramanian, and Dan Roth. “An Improved Neural Baseline for Temporal Relation Extraction.” EMNLP, 2019 (short paper). [website] [poster] [github]
  12. 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. [pdf] [slides]
    • Understanding text summarization from a perspective of information theory.
  13. Qiang Ning, Hangfeng He, Chuchu Fan, and Dan Roth. “Partial or Complete, That’s The Question.” NAACL, 2019. [website] [pdf] [poster]
  14. Qiang Ning, Ben Zhou, Zhili Feng, Haoruo Peng, and Dan Roth. “CogCompTime: A Tool for Understanding Time in Natural Language Text.” EMNLP, 2018 (demo paper). [website] [pdf] [poster] [online demo][github].
    • A state-of-the-art automatic tool for:
    • Time expression extraction and normalization
    • Temporal relation extraction
  15. 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
  16. Qiang Ning, Zhili Feng, Hao Wu, and Dan Roth. “Joint Reasoning for Temporal and Causal Relations.” ACL, 2018. [website] [pdf] [talk] [github:TCR]
  17. 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
  18. 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)
  19. 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)

Talks and posters

  1. Faculty candidate talks at Duke, May 8, 2019, and at Rice, Apr 25, 2019. [ppt; 80MB]
  2. Job talk at AI2. Jan 25, 2019. [ppt][video]
  3. A Multi-Axis Annotation Scheme for Event Temporal Relations. ACL, 2018. [ppt]
  4. Joint Reasoning for Temporal and Causal Relations. ACL, 2018. [ppt]
  5. A Structured Learning Approach to Temporal Relation Extraction. EMNLP, 2017. [ppt]

Signal processing (<=2016)

  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

My technotes

  1. Roadmap of word embedding techniques and how that leads to BERT [pdf] [pptx]
  2. A brief review of tensor decomposition [pdf]
  3. A brief review of graph2vec [pdf]
  4. A brief review of some bounds [pdf]
  5. Cramer-Rao bounds and its application to spectral quantification in MRS [pdf]
  6. A brief review of matrix derivatives and descend optimization methods [pdf]
  7. Gradient calculation for nonlinear least squares problems with complex numbers [pdf]
  8. A proof of the VARiable PROjection (VARPRO) method in Hilbert space [pdf]
  9. An introduction to one-class classification. [pdf]
  10. Qualifying Exam [pdf]

Patent

  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)
  • Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL)
  • AAAI Conference on Artificial Intelligence (AAAI)
  • The International Conference on Computational Linguistics (COLING)
  • International Conference on Language Resources and Evaluation (LREC)
  • European Conference on Information Retrieval (ECIR)
  • Journal of Artificial Intelligence Research (JAIR)
  • Journal of Natural Language Engineering (JNLE)
  • NUSE Workshop, ACL 2020
  • IEEE Signal Processing Letters (SPL)
  • IEEE Transactions on Biomedical Engineering (TBME)
  • Magnetic Resonance Imaging (MRM)