Synopses & Reviews
This book provides a systematic, foundational introduction to automatic alignment of parallel texts, a family of essential corpus analysis techniques for computing and learning the mappings between corresponding parts of the texts. Bitext alignment lies at the heart of all data-driven machine learning approaches to automatic translation, and the rapid research progress on alignment during the past two decades underlies the success of statistical machine translation approaches.
Bi-text alignment lies at the heart of all data-driven machine-learning approaches to automatic translation and is an essential technique of analysis. This book provides a systematic, foundational introduction to the automatic alignment of parallel texts.
About the Author
Prof. Wu received his PhD in Computer Science from the University of California at Berkeley, and was a postdoctoral fellow at the University of Toronto (Ontario, Canada) prior to joining HKUST in 1992. He received his Executive MBA from Kellogg and HKUST in 2002, and a BS in Computer Engineering from the University of California at San Diego (Revelle College departmental award, cum laude, Phi Beta Kappa) in 1984. He has been a visiting researcher at Columbia University in 1995-96, Bell Laboratories in 1995, and the Technische Universität München (Munich, Germany) during 1986-87. Prof. Wu serves as Associate Editor of AI Journal and on the Editorial Board of Machine Translation and Journal of Natural Language Engineering. He has also served as Co-Chair for EMNLP-2004, and on the Editorial Board of Computational Linguistics and as Associate Editor of ACM Transactions on Speech and Language Processing, the Organizing Committee of ACL-2000 and WVLC-5 (SIGDAT 1997), and the Executive Committee of the Association for Computational Linguistics (ACL).