Conference and workshop papers

  1. Lippincott, T., & Van Durme, B. (2021). Using negative evidence for language classification. Under Review.
  2. Lippincott, T. (2020). StarCoder: A general neural ensemble technique to support traditional scholarship, illustrated with a study of the post-Atlantic slave trade. Proceedings of Digital Humanities 2020.
  3. Lippincott, T. (2019). Graph convolutional networks for exploring authorship hypotheses. Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature.
  4. Lippincott, T., Shapiro, P., Duh, K., & McNamee, P. (2019). JHU System Description for the MADAR Arabic Dialect Identification Shared Task. Proceedings of the Fourth Workshop for Arabic Natural Language Processing.
  5. Lippincott, T., & Carrell, A. (2018). Observational Comparison of Geo-tagged and Randomly-drawn Tweets. PEOPLES: NAACL Workshop.
  6. Lippincott, T. (2018). Portable, layer-wise task performance monitoring for NLP models. BlackBox: EMNLP Workshop.
  7. Lippincott, T., Naradowsky, J., & Van Durme, B. (2018). Recognizing self-identification in social media. North American Social Networks Conference.
  8. Niehues, J., Lippincott, T., Martindale, M., Varis, D., & Duh, K. (2018). Understanding Continued Training of Neural Machine Translation Models. Under Review.
  9. Lippincott, T. (2017). Author Attribute Classification for Social Media. North American Social Networks Conference.
  10. Lippincott, T., & Van Durme, B. (2016). Fluency detection on communication networks. EMNLP, 1025–1029.
  11. Rasooli, M. S., Lippincott, T., Habash, N., & Rambow, O. (2014). Unsupervised Morphology-Based Vocabulary Expansion. ACL (1), 1349–1359.
  12. Lippincott, T., Séaghdha, D. O., & Korhonen, A. (2012). Learning syntactic verb frames using graphical models. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers-Volume 1, 420–429.
  13. Lippincott, T., Séaghdha, D. O., Sun, L., & Korhonen, A. (2010). Exploring variations across biomedical subdomains. Proceedings of the 23rd International Conference on Computational Linguistics, 689–697.
  14. Lippincott, T., & Passonneau, R. J. (2009). Semantic Clustering for a Functional Text Classification Task. CICLing, 509–522.
  15. Lippincott, T. (2008). A Framework for Multilayered Boundary Detection. Digital Humanities 2009.
  16. Passonneau, R. J., Yano, T., Lippincott, T., & Klavans, J. (2008). Relation between agreement measures on human labeling and machine learning performance: Results from an art history image indexing domain. Computational Linguistics for Metadata Building, 49.
  17. Passonneau, R., Yano, T., Lippincott, T., & Klavans, J. (2008). Functional semantic categories for art history text: human labeling and preliminary machine learning. International Conference on Computer Vision Theory and Applications, Workshop 3: Metadata Mining for Image Understanding.
  18. Klavans, J., Sheffield, C., Abels, E., Beaudoin, J. E., Jenemann, L., Lin, J., Lippincott, T., Passonneau, R., Sidhu, T., Soergel, D., & others. (2008). Computational linguistics for metadata building: Aggregating text processing technologies for enhanced image access.

Journal articles

  1. Lippincott, T., Rimell, L., Verspoor, K., & Korhonen, A. (2013). Approaches to verb subcategorization for biomedicine. Journal of Biomedical Informatics, 46(2), 212–227.
  2. Rimell, L., Lippincott, T., Verspoor, K., Johnson, H. L., & Korhonen, A. (2013). Acquisition and evaluation of verb subcategorization resources for biomedicine. Journal of Biomedical Informatics, 46(2), 228–237.
  3. Lippincott, T., Séaghdha, D. Ó., & Korhonen, A. (2011). Exploring subdomain variation in biomedical language. BMC Bioinformatics, 12(1), 212.

Monographs

  1. Lippincott, T. (2015). Unsupervised approaches to syntactic verb frame acquisition for biomedicine [PhD thesis]. University of Cambridge.

Demos and Presentations

  1. CADET: Computer Assisted Discovery Extraction and Translation. (2017). In The 8th International Joint Conference on Natural Language Processing.
  2. Innovative Courses at Johns Hopkins University: Creating Links to Industry, Business, Community, and Real-World Cases. 15th International Conference on Higher Education Reform.