Recurrent Neural Network based Probabilistic Language Model

Language: English

Published by AV Akademikerverlag Okt 2017, 2017

620220544X / 9786202205443

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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.

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This item is printed on demand - it takes 3-4 days longer - Neuware -Statistical n-gram language models are widely used for their state of the art performance in a continuous speech recognition system. In a domain based scenario, the sequences vary at large for expressing same context by the speakers. But, holding all possible sequences in training corpora for estimating n-gram probabilities is practically difficult. Capturing long distance dependencies from a sequence is an important feature in language models that can provide non zero probability for a sparse sequence during recognition. A simpler back-off n-gram model has a problem of estimating the probabilities for sparse data, if the size of n gram increases. Also deducing knowledge from training patterns can help the language models to generalize on an unknown sequence or word by its linguistic properties like noun, singular or plural, novel position in a sentence. For a weaker generalization, n-gram model needs huge sizes of corpus for training. A simple recurrent neural network based language model approach is proposed here to efficiently overcome the above difficulties for domain based corpora. 60 pp. Englisch.

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Bibliographic details

Title
Recurrent Neural Network based Probabilistic Language Model
Author
Sathyanarayanan Kuppusami
Publisher
AV Akademikerverlag Okt 2017
Publication year
2017
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
620220544X
ISBN 13
9786202205443
Item weight
107 grams
Dimensions
220x150x4 mm

BuchWeltWeit Ludwig Meier e.K.

Bergisch Gladbach, Germany

5-star seller

AbeBooks seller since January 11, 2012

Shipping rates from Germany to U.S.A.

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BuchWeltWeit Ludwig Meier e.K.

Germany