Friday, April 13 2:30 - 3:30 PM 113 IST Bldg. (Cybertorium) Tom Mitchell E. Fredkin University Professor Chair, Machine Learning Department School of Computer Science Carnegie Mellon University JAMES F. KELLY DISTINGUISHED Series Never-Ending Language Learning We describe our effort to build a Never-Ending Language Learner (NELL) that runs 24 hours per day, forever, learning to read the Web. Each day NELL extracts (reads) more facts from the Web, and integrates these into its growing knowledge base of beliefs. Each day NELL also learns to read better than yesterday, enabling it to go back to the text it read yesterday, and extract more facts, more accurately. NELL has now been running 24 hours a day for more than two years. The result so far is a collection of 15 million interconnected beliefs [e.g., servedWtih(coffee, applePie), isA(applePie, bakedGood)], that NELL is considering at different levels of confidence, along with hundreds of thousands of learned phrasings, morphological features, and Web page structures that NELL uses to extract beliefs from the Web. The approach implemented in NELL is based on three key ideas: (1) coupling the semi- supervised training of thousands of different functions that extract different types of information from different Web sources, (2) automatically discovering new constraints that more tightly couple the training of these functions over time, and (3) a curriculum or sequence of increasing difficult learning tasks. Track NELL’s progress at http://rtw.ml.cmu.edu. LECTURE 2012 Department of Computer Science and Engineering Penn State is committed to affirmative action, equal opportunity, and the diversity of its workforce. U.Ed. ENG 12-29
James F. Kelly Distinguished Lecturer
Mar 30, 2016
James F. Kelly Distinguished Lecturer poster for spring 2012. CSE Dept. at Penn State.
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Friday, April 13
2:30 - 3:30 PM
113 IST Bldg. (Cybertorium)
Tom MitchellE. Fredkin University ProfessorChair, Machine Learning DepartmentSchool of Computer ScienceCarnegie Mellon University
JAMES F. KELLY DISTINGUISHED
SeriesNever-Ending Language Learning
We describe our effort to build a Never-Ending Language Learner (NELL) that runs 24 hours per day, forever, learning to read the Web. Each day NELL extracts (reads) more facts from the Web, and integrates these into its growing knowledge base of beliefs. Each day NELL also learns to read better than yesterday, enabling it to go back to the text it read yesterday, and extract more facts, more accurately.
NELL has now been running 24 hours a day for more than two years. The result so far is a collection of 15 million interconnected beliefs [e.g., servedWtih(coffee, applePie), isA(applePie, bakedGood)], that NELL is considering at different levels of confidence, along with hundreds of thousands of learned phrasings, morphological features, and Web page structures that NELL uses to extract beliefs from the Web.
The approach implemented in NELL is based on three key ideas: (1) coupling the semi-supervised training of thousands of different functions that extract different types of information from different Web sources, (2) automatically discovering new constraints that more tightly couple the training of these functions over time, and (3) a curriculum or sequence of increasing difficult learning tasks. Track NELL’s progress at http://rtw.ml.cmu.edu.
LECTURE
2012
Department of Computer Science and Engineering
Penn State is committed to affirmative action, equal opportunity, and the diversity of its workforce.
U.Ed. ENG 12-29
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