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Stochastically-Based Semantic Analysis
Minker, Wolfgang; Waibel, Alex; Mariani, Joseph
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Description for Stochastically-Based Semantic Analysis
Paperback. Series: The Springer International Series in Engineering and Computer Science. Num Pages: 238 pages, biography. BIC Classification: UYQ; UYQL. Category: (P) Professional & Vocational. Dimension: 234 x 156 x 13. Weight in Grams: 379.
Stochastically-Based Semantic Analysis investigates the problem of automatic natural language understanding in a spoken language dialog system. The focus is on the design of a stochastic parser and its evaluation with respect to a conventional rule-based method.
Stochastically-Based Semantic Analysis will be of most interest to researchers in artificial intelligence, especially those in natural language processing, computational linguistics, and speech recognition. It will also appeal to practicing engineers who work in the area of interactive speech systems.
Stochastically-Based Semantic Analysis investigates the problem of automatic natural language understanding in a spoken language dialog system. The focus is on the design of a stochastic parser and its evaluation with respect to a conventional rule-based method.
Stochastically-Based Semantic Analysis will be of most interest to researchers in artificial intelligence, especially those in natural language processing, computational linguistics, and speech recognition. It will also appeal to practicing engineers who work in the area of interactive speech systems.
Product Details
Format
Paperback
Publication date
2012
Publisher
Springer-Verlag New York Inc. United States
Number of pages
238
Condition
New
Series
The Springer International Series in Engineering and Computer Science
Number of Pages
221
Place of Publication
New York, NY, United States
ISBN
9781461373964
SKU
V9781461373964
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
Reviews for Stochastically-Based Semantic Analysis
`Altogether the book is well worth reading. The research reported in this book is very interesting, and the authors managed to almost find the right level of detail in their descriptions, enabling the reader to understand how a system works without losing the overall picture.' Natural Language Engineering, 7:1 (2001)