Background papers on word embedding methods used on this site

Latent Semantic Analysis

• Landauer, T. K., & Dumais, S. T. (1997). A solution to Plato's problem: The latent semantic analysis theory of acquisition, induction, and representation of knowledge. Psychological review, 104(2), 211.
• Landauer, T. K, Foltz, P. W., & Laham, D. (1998). An introduction to Latent Semantic Analysis. Discourse Processes, 25(2&3), 259-284.
• Foltz, P. W. (1996) Latent Semantic Analysis for text-based research. Behavior Research Methods, Instruments and Computers. 28(2), 197-202.

word2vec

• Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 26.
• Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space. ArXiv:1301.3781 [Cs].

BERT

• Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171–4186, Minneapolis, Minnesota. Association for Computational Linguistics.

Lab papers

These papers are a sample of the research being conducted at the University of Colorado on applying machine learning and natural language processing methods that use these word embedding and other language processing techniques as the basis for the approaches. More papers can be found here.

Mental Health Assessment

• Chandler, C., Cheng, J., Foltz, P.W., Holmlund, T., Cohen, A. S.,& Elvevåg, B. E. (2020). Machine learning for ambulatory applications of neuropsychological testing. Intelligence-Based Medicine, 1, 100006.
• Chandler, C., Foltz, P. W., Cheng, J., Bernstein, J. C., Rosenfeld, E. P., Cohen, A. S., Holmlund, T. B., & Elvevåg, B. (2019). Overcoming the bottleneck in traditional assessments of verbal memory: Modeling human ratings and classifying clinical group membership. Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology, 137–147.
• Shreevastava, S., & Foltz, P. (2021). Detecting Cognitive Distortions from Patient-Therapist Interactions. In Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access (pp. 151-158).
• Elvevåg, B., Foltz, P. W., Weinberger, D. R. & Goldberg, T. E. (2007) Quantifying incoherence in speech: An automated methodology and novel application to schizophrenia. Schizophrenia Research.

Automated Assessment of Writing

• Foltz, P. W. (2020). Practical Considerations for Using AI models in Automated Scoring of Writing. In H. Jiao & R. W. Lissitz (Eds.). Applications of Artificial Intelligence to Assessment. Charlotte, NC: Information Age Publisher.
• Foltz, P. W., Streeter, L. A., Lochbaum, K. E., & Landauer, T. K (2013). Implementation and applications of the Intelligent Essay Assessor. Handbook of Automated Essay Evaluation, M. Shermis & J. Burstein, (Eds.). Pp. 68-88. Routledge, NY.

Team Collaborative Assessment

• Southwell, R., Pugh, S., Perkoff E. M., Clevenger, C., Bush, J., Lieber, R., Ward, W., Foltz, P., D'Mello, S. (2022). Challenges and feasibility of automatic speech recognition for modeling student collaborative discourse in classrooms. In: Proceedings of the 15th Educational Data Mining. Springer.
• Graesser, A. C., Fiore, S. M., Greiff, S., Andrews-Todd, J., Foltz, P. W., & Hesse, F. W. (2018). Advancing the Science of Collaborative Problem Solving. Psychological Science in the Public Interest, 19(2), 59–92.
• Foltz, P. W. & Martin, M. J. (2008). Automated Communication Analysis of Teams. In E. Salas, G. F. Goodwin, & S. Burke (Eds.) Team Effectiveness in Complex Organizations and Systems: Cross-disciplinary perspectives and approaches. New York: Routledge.