My research falls under the heading of Natural Language Processing which is itself a sub-discipline of Artificial Intelligence (AI). In my research, I develop algorithms that try to "understand" text in the same ways that you (presumably a human) understand text. My interest in natural language comes from an appreciation of the brain's ability to effortlessly produce and understand written and spoken language. We take for granted our ability to read a newspaper or recommend a book to a friend. However, these mundane tasks belie the brain's incredible capacity for storing, processing, and generalizing information.
Currently, I'm working on a few different projects: dialogue systems in open-domain conversational agents and semantic representations (i.e. how to capture the meaning while minimizing other counfounding factors such as word choice). The techniques that I use in my research come from various sub-disciplines of AI including graphical models, statistical pattern recognition, gradient-based optimization, supervised learning, and natural language processing. Many of these techniques are grounded in probability and statistics.
- A. Lamar and A. Chambers. Generating metrically accurate homeric poetry with recurrent neural networks. International Journal of Transdisciplinary AI, 2020. In Press.
- A. Lamar and A. Chambers. Generating homeric poetry with deep neural networks. Proceedings of the 1st International Transdiscplinary A. I. Conference, 2019, pp. 68-75. [pdf]
- A. Chambers. Statistical models for text classification: Applications and analysis, Ph.D., University of California, Irvine (2013). ProQuest Dissertations and Theses. [pdf]
- T. Rubin, A. Chambers, P. Smyth, and M. Steyvers. Statistical topic models for multi-label document classification. Machine Learning, 2011.
- A. Chambers and P. Smyth. Learning concept graphs from text with stick-breaking priors. Advances in Neural Information Processing Systems (NIPS), 2010.
- C. Chemudugunta, A. Holloway, P. Smyth, and M. Steyvers. Modeling documents by combining semantic concepts with unsupervised statistical learning. Int'l. Semantic Web. Conf. (ISWC), LNCS 5318, Springer, 2008, pp.229-224.
- A. Holloway and T.-Y. Chen. Neural networks for predicting the behavior of preconditioned iterative solvers. Proc. of the 26th Int'l. Conf. Compt'l. Science (ICCS), LNCS 4487, Springer, 2007, pp. 302-309
- Corpus of sentences from scientific articles labelled according to a modified Argumentative Zones annotation scheme. [zip][README]