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Shallow Parsing with Conditional Random Fields plus an overview of recent work in Bioinformatics at UPenn


Fernando Pereira
UPenn

Abstract


Conditional random fields for sequence labeling offer advantages over both generative models like HMMs and classifiers applied at each sequence position. Among sequence labeling tasks in language processing, shallow parsing has received much attention, with the development of standard evaluation datasets and extensive comparison among methods. We show here how to train a conditional random field to achieve performance as good as any reported base noun-phrase chunking method on the CoNLL task, and better than any reported single model. Improved training methods based on modern optimization algorithms were critical in achieving these results. We present extensive comparisons between models and training methods that confirm and strengthen previous results on shallow parsing and training methods for maximum-entropy models.

The second half of the talk will change topic, and consist of an informal overview of recent work at UPenn in bioinformatics---both the statistical modeling of biological sequences, and information extraction from Medline text.

Joint work with Fei Sha, and others at UPenn.

Bio:

Fernando Pereira is the Andrew and Debra Rachleff Professor, and Department Chair at the Department of Computer and Information Science at the University of Pennsylvania. During 1987-88, he headed SRI's Cambridge, England, research center. He joined AT&T in the summer of 1989, were worked on speech recognition, speech retrieval, probabilistic language models, and several other topics. From 1994 to 2000, he headed the Machine Learning and Information Retrieval department of AT&T Labs--Research. He spent the 2000-2001 academic year as a research scientist at WhizBang! Labs, where he developed finite-state models and algorithms for information extraction from the Web. He is the author of over 70 scientific publications on logic, machine learning, speech recognition, and language modeling.

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