Period disambiguation using a neural network

T. Humphrey, Fu-qiu Zhou
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引用次数: 17

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.<>
基于神经网络的周期消歧
只提供摘要形式。一个从未在文献中解决的问题是现实世界文档中句子的机器识别问题(即识别句子的开头和结尾)。给出了问题的描述和实验,表明可以使用反向传播学习算法训练前馈神经网络来消除周期的歧义。作者还提出了一些结果,表明较低的训练容忍度可以提高神经网络的泛化能力,但代价是训练迭代次数的急剧增加。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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