Research

  1. Li, W., Chen, Y., & Lalor, J. (2023). Stars Are All You Need: A Distantly Supervised Pyramid Network for Document-Level End-to-End Sentiment Analysis. arXiv preprint arXiv:2305.01710.
  2. Lalor, J., Wu, H., Mazor, K., & Yu, H. (2023). Evaluating the efficacy of NoteAid on EHR note comprehension among US Veterans through Amazon Mechanical Turk. International Journal of Medical Informatics.
  3. Wowak, K., Lalor, J., Somanchi, S., & Angst, C. (2023). Business Analytics in Healthcare: Past, Present, and Future Trends. Manufacturing & Service Operations Management.
  4. Lalor, J. & Rodriguez, P. (2023). py-irt: A scalable item response theory library for python. INFORMS Journal on Computing.
  5. Lalor, J., Yang, Y., Smith, K., Forsgren, N., & Abbasi, A. (2022). Benchmarking intersectional biases in NLP. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies.
  6. Lalor, J. & Guo, H. (2022). Measuring algorithmic interpretability: A human-learning-based framework and the corresponding cognitive complexity score. arXiv preprint arXiv:2205.10207.
  7. Rodriguez, P., Htut, P., Lalor, J., & Sedoc, J. (2022). Clustering Examples in Multi-Dataset Benchmarks with Item Response Theory. Proceedings of the Third Workshop on Insights from Negative Results in NLP.
  8. Abbasi, A., Dobolyi, D., Lalor, J., Netemeyer, R., Smith, K., & Yang, Y. (2021). Constructing a psychometric testbed for fair natural language processing. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
  9. Safadi, H., Lalor, J., & Berente, N. (2021). The effect of bots on human interaction in online communities. .
  10. Berente, N., Lalor, J., Somanchi, S., & Abbasi, A. (2021). The Illusion of Certainty and Data-Driven Decision Making in Emergent Situations. .
  11. Lalor, J., Hu, W., Tran, M., Wu, H., Mazor, K., & Yu, H. (2021). Evaluating the effectiveness of NoteAid in a community hospital setting: randomized trial of electronic health record note comprehension interventions with patients. Journal of medical Internet research.
  12. Rodriguez, P., Barrow, J., Hoyle, A., Lalor, J., Jia, R., & Boyd-Graber, J. (2021). Evaluation examples are not equally informative: How should that change NLP leaderboards?. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers).
  13. Ma, M. & Lalor, J. (2020). An empirical analysis of human-bot interaction on reddit. Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020).
  14. Lalor, J. & Yu, H. (2020). Dynamic data selection for curriculum learning via ability estimation. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing.
  15. Lalor, J. (2020). Learning latent characteristics of data and models using item response theory. .
  16. Lalor, J., Berente, N., & Safadi, H. (2020). Bots versus humans in online social networks: a study of Reddit communities. .
  17. Lalor, J., Wu, H., & Yu, H. (2019). Learning latent parameters without human response patterns: Item response theory with artificial crowds. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing.
  18. Cho, E., Xie, H., Lalor, J., Kumar, V., & Campbell, W. (2019). Efficient semi-supervised learning for natural language understanding by optimizing diversity. 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU).
  19. Chen, J., Lalor, J., Liu, W., Druhl, E., Granillo, E., Vimalananda, V., & Yu, H. (2019). Detecting hypoglycemia incidents reported in patients’ secure messages: using cost-sensitive learning and oversampling to reduce data imbalance. Journal of medical Internet research.
  20. Lalor, J., Woolf, B., & Yu, H. (2019). Improving electronic health record note comprehension with NoteAid: randomized trial of electronic health record note comprehension interventions with crowdsourced workers. Journal of medical Internet research.
  21. Lalor, J., Wu, H., Munkhdalai, T., & Yu, H. (2018). Understanding deep learning performance through an examination of test set difficulty: A psychometric case study. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing.
  22. Lalor, J., Wu, H., Chen, L., Mazor, K., & Yu, H. (2018). ComprehENotes, an instrument to assess patient reading comprehension of electronic health record notes: development and validation. Journal of medical Internet research.
  23. Lalor, J., Wu, H., & Yu, H. (2017). CIFT: Crowd-informed fine-tuning to improve machine learning ability. arXiv preprint arXiv:1702.08563.
  24. Munkhdalai, T., Lalor, J., & Yu, H. (2016). Citation analysis with neural attention models. Proceedings of the Seventh International Workshop on Health Text Mining and Information Analysis.
  25. Lalor, J., Wu, H., & Yu, H. (2016). Building an evaluation scale using item response theory. Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing.
  26. Settle, A., Lalor, J., & Steinbach, T. (2015). Evaluating a linked-courses learning community for development majors. Proceedings of the 16th annual conference on information technology education.
  27. Settle, A., Lalor, J., & Steinbach, T. (2015). A computer science linked-courses learning community. Proceedings of the 2015 ACM Conference on Innovation and Technology in Computer Science Education.
  28. Miller, C., Settle, A., & Lalor, J. (2015). Learning Object-Oriented Programming in Python: Towards an Inventory of Difficulties and Testing Pitfalls. .
  29. Settle, A., Lalor, J., & Steinbach, T. (2015). Reconsidering the impact of CS1 on novice attitudes. Proceedings of the 46th ACM Technical Symposium on Computer Science Education.