DocumentCode :
3253912
Title :
Period disambiguation using a neural network
Author :
Humphrey ; Zhou
Author_Institution :
Dept. of Comput. Sci., Wright State Univ., Dayton, OH, USA
fYear :
1989
fDate :
0-0 1989
Abstract :
Summary form only given. A problem that has never been addressed in the literature is the problem of machine recognition of sentences in real-world documents (i.e. identifying the beginning and end of a sentence). A description is given of the problem and experiments that show that a feedforward neural network can be trained, using the backpropagation learning algorithm, to disambiguate periods. The authors also present results that indicate that a low training tolerance improves a neural network´s ability to generalize at the cost of a dramatic increase in the number of training iterations.<>
Keywords :
computerised pattern recognition; learning systems; neural nets; backpropagation learning algorithm; feedforward neural network; low training tolerance; machine recognition of sentences; neural network; period disambiguation; real-world documents; sentence beginning recognition; sentence end recognition; training iterations; Learning systems; Neural networks; Pattern recognition;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1989. IJCNN., International Joint Conference on
Conference_Location :
Washington, DC, USA
Type :
conf
DOI :
10.1109/IJCNN.1989.118427
Filename :
118427
Link To Document :
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