Handbook of natural language processing and machine translation : DARPA global autonomous language exploitation
Author(s)
Bibliographic Information
Handbook of natural language processing and machine translation : DARPA global autonomous language exploitation
Springer, c2011
Available at 17 libraries
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Note
Includes bibliographical references
Description and Table of Contents
Description
This comprehensive handbook, written by leading experts in the field, details the groundbreaking research conducted under the breakthrough GALE program--The Global Autonomous Language Exploitation within the Defense Advanced Research Projects Agency (DARPA), while placing it in the context of previous research in the fields of natural language and signal processing, artificial intelligence and machine translation.
The most fundamental contrast between GALE and its predecessor programs was its holistic integration of previously separate or sequential processes. In earlier language research programs, each of the individual processes was performed separately and sequentially: speech recognition, language recognition, transcription, translation, and content summarization. The GALE program employed a distinctly new approach by executing these processes simultaneously. Speech and language recognition algorithms now aid translation and transcription processes and vice versa. This combination of previously distinct processes has produced significant research and performance breakthroughs and has fundamentally changed the natural language processing and machine translation fields.
This comprehensive handbook provides an exhaustive exploration into these latest technologies in natural language, speech and signal processing, and machine translation, providing researchers, practitioners and students with an authoritative reference on the topic.
Table of Contents
Data Acquisition and Linguistic Resources.- Machine Translation from Text.- Machine Translation From Speech.- Distillation.- Machine Translation Evaluation and Optimization.- Operational Engines.
by "Nielsen BookData"