Daniel Klein

Digital Library

ACM Grace Murray Hopper Award

USA - 2006

citation

For the design of a system capable of learning a high-quality grammar for English directly from text.

Dr. Klein's work on the automatic organization of natural language information has led to the design of the first machine learning system capable of learning a high-quality grammar for English directly from text without human annotations or supervision. Unsupervised learning of natural language structure is an important step towards getting computers to understand natural languages, a goal of artificial intelligence and computational linguistics research since the 1950s.

Dr. Klein's broader research on learning linguistic structure has produced the most accurate grammatical analysis system in the world for a variety of languages. This system is currently being used by several other research groups to improve language processing applications, including the state-of-the-art machine translation system at the University of Southern California's Information Sciences Institute. Dr. Klein's group is now developing novel grammar-based models of machine translation.

An assistant professor of Computer Science at UCB, Dr. Klein is also a manager of Talking Dolphin, a provider of software and services that enable sophisticated processing of text documents. His current research focuses on the automatic organization of natural language information, including models of information extraction, machine translation, and speech recognition. He has been recognized by a Marshall Scholarship, a Sloan Fellowship, a Microsoft New Faculty Fellowship, and a National Science Foundation Career Award.