Chris Brew

Chris Brew
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Main area of research
Natural language & AI

Chris Brew is a Natural Language Processing Research Scientist in the Linguistic Technologies group at Nuance. Previously, he was a Senior Research Scientist with the Educational Testing Service in Princeton, where he was the scientific lead for the c-rater project on automated short answer grading. He has been active in Natural Language Processing for nearly 30 years, first in the UK, where he designed and taught the University of Edinburgh’s graduate class in Data-Intensive Linguistics (one of the first ever classes in Statistical Natural Language) processing, then as Associate Professor of Linguistics and Computer Science at The Ohio State University, where he co-directed the Speech and Language Technologies Laboratory, as well as the Computational Linguistics Program. He holds a B.Sc in Chemistry from the University of Bristol, an M.Sc in Experimental Psychology from the University of Sussex. His doctoral research, also at Sussex, advised by Stephen Isard, led to a completed dissertation on computational modeling of parsing in dialogue. He is the co-author of a 2012 textbook on Language and Computers, was main advisor on 17 Ph.D dissertations, and has published on a wide range of topics in speech and language technologies.

Selected articles

Towards effective tutorial feedback for explanation questions: a dataset and baselines

We propose a new shared task on grading student answers with the goal of enabling well targeted and flexible feedback in a tutorial dialogue setting.

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CCG syntactic reordering models for phrase-based machine translation

Statistical phrase-based machine translation requires no linguistic information beyond word-aligned parallel corpora (Zens et al., 2002; Koehn et al., 2003). Unfortunately, this linguistic agnosticism often

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Multilingual animacy classification by sparse logistic regression

This paper presents results from three experiments on automatic animacy classification in Japanese and English. We present experiments that focus on solutions to the problem

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Sentiment analysis of suicide notes: a shared task

This paper reports on a shared task involving the assignment of emotions to suicide notes. Two features distinguished this task from previous shared tasks in

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Semantic role labeling without treebanks?

We describe a method for training a semantic role labeler for CCG in the absence of gold-standard syntax derivations. Traditionally, semantic role labeling is performed

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