Stochastic semantic analysis

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Stochastic semantic analysis is an approach used in computer science as a semantic component of natural language understanding.

Stochastic models generally use the definition of segments of words as basic semantic units for the semantic models, and in some cases involve a two layered approach.[1]

Example applications have a wide range. In machine translation, it has been applied to the translation of spontaneous conversational speech among different languages.[2] In the area of spoken language understanding the fact that spoken sentences often do not follow the grammar of a language and involve self-corrections, repetitions, and other irregularities, the use of stochastic semantic has been suggested as a natural fit to achieve robustness to deal with noise due to the spontaneous nature of spoken language.[3]

References[edit]

  • Stochastically-based semantic analysis by Wolfgang Minker, Alex Waibel, Joseph Mariani 1999 ISBN 0-7923-8571-3

Notes[edit]

  1. ^ Language Understanding Using Two-Level Stochastic Models by F. Pla, et al, 2001, Springer Lecture Notes in Computer Science ISBN 978-3-540-42557-1
  2. ^ W. Minkera, M. Gavaldàb and A. Waibel Stochastically-based semantic analysis for machine translation in Computer Speech & Language Volume 13, Issue 2, April 1999, Pages 177-194
  3. ^ R. De Mori et al, Spoken language understanding in IEEE Signal Processing Magazine, May 2008 Volume: 25 Issue: 3, pages 50 - 58 ISSN 1053-5888