Title :
Period disambiguation using a neural network
Author_Institution :
Dept. of Comput. Sci., Wright State Univ., Dayton, OH, USA
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;
Conference_Titel :
Neural Networks, 1989. IJCNN., International Joint Conference on
Conference_Location :
Washington, DC, USA
DOI :
10.1109/IJCNN.1989.118427