Byron Wallace,

University of Texas at Austin


Text classification is a fundamental natural language processing (NLP) task. Modern neural models that exploit (usually pre-trained) word embeddings have recently achieved impressive results on such tasks. Feed-forward Convolutional Neural Networks (CNNs), in particular, have emerged as a relatively simple yet powerful class of models for text classification, often outperforming more complex recurrent neural models such as Long Term Short Term networks (LSTMs). In this talk, I will review CNN architectures appropriate for text and discuss model design and hyper-parameter trade-offs. I will then introduce new variants of CNNs, including an architecture that jointly exploits multiple sets of embeddings and a model that capitalizes on “rationale-level” supervision, i.e., labels on sentences concerning their relevance to the classification task at hand. Finally, I will present recent work on “active learning” approaches for CNNs that aim to rapidly induce discriminative embeddings with as few labels as possible. I will present results with respect to diverse text classification tasks, ranging from verbal irony detection to biomedical text classification.


Byron Wallace is currently an assistant professor in the School of Information and the Department of Computer Science (by courtesy) at the University of Texas at Austin. (He will be joining the College of Computer and Information Science at Northeastern University in fall 2016.) He holds a PhD in Computer Science from Tufts University, where he was advised by Carla Brodley. Prior to joining UT, he was research faculty at Brown University, where he was part of the Center for Evidence-Based Medicine and also affiliated with the Brown Laboratory for Linguistic Information Processing (BLLIP). His primary research is in machine learning and natural language processing methods, with an emphasis on their application in health informations (and especially evidence-based medicine). Wallace’s work has been supported by multiple grants from the National Institutes for Health (NIH), the National Science Foundation (NSF), and the Army Research Office (ARO). He won the Tufts University 2012 Outstanding Graduate Researcher award and his thesis work was recognized as The Runner Up for the 2013 ACM Special Interest Group on Knowledge Discovery and Data Mining (SIG KDD) Dissertation Award. He recently co-authored the winning submission for the Health Care Data Analytics Challenge at the 2015 IEEE International Conference on Healthcare Informatics.


Date: 2016-Jun-22     Time: 14:30:00     Room: 336

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