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Archive for the ‘Text Mining’ Category

lafferty-mansueto

John Lafferty in the Mansueto Library. (Photo by Jason Smith)

Learning a subject well means moving beyond the recitation of facts to a deeper knowledge that can be applied to new problems. Designing computers that can transcend rote calculations to more nuanced understanding has challenged scientists for years. Only in the past decade have researchers’ flexible, evolving algorithms—known as machine learning—matured from theory to everyday practice, underlying search and language-translation websites and the automated trading strategies used by Wall Street firms.

These applications only hint at machine learning’s potential to affect daily life, according to John Lafferty, the Louis Block Professor in Statistics and Computer Science. With his two appointments, Lafferty bridges these disciplines to develop theories and methods that expand the horizon of machine learning to make predictions and extract meaning from data.

“Computer science is becoming more focused on data rather than computation, and modern statistics requires more computational sophistication to work with large data sets,” Lafferty says. “Machine learning draws on and pushes forward both of these disciplines.”

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(from Wikimedia Commons)Newspapers don’t always have the most exciting afterlife. A day or two after printing, most newspapers retire to a secondary role as kindling for the fireplace, stuffing for fragile items or a disposable surface for house-training pets. But the content of newspaper articles can have value long after publication for researchers interested in the daily, local pulse of a particular subject. Traditionally, information was extracted from old newspaper clippings by arduously crawling through endless microfiche files or (more recently) web pages. But new methods for text mining offer a fast, automated way to turn old newspaper articles into valuable information — which can then be poured into even more ambitious project.

Those methods were the backbone of a talk at the Computation Institute by John T. Murphy of the CI and Argonne National Laboratory’s Decision and Information Sciences Division. An anthropologist, Murphy is interested in the ways that towns in the American west handle water management — a utility that many of us take for granted, but which can be a bitter political battlefield. To sum up these disputes, Murphy referenced a quote often attributed, albeit probably falsely, to Mark Twain: “Whiskey is for drinking, water is for fighting over.”

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