Method for correcting wrongly written characters for Chinese character spelling on basis of CNN-LSTM (convolutional neural networks-long-short term memories)
A technology for typos and Chinese characters, applied in the field of computer natural language processing, can solve problems such as inability to correct typos, and achieve the effect of improving accuracy
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[0026] Such as figure 1 As shown, a CNN-LSTM-based Chinese spelling typo correction method includes the following steps:
[0027] A: The encoding part. The function of the encoding part is mainly to encode the input sentence and filter typos. Specifically, the following steps are included:
[0028] A1: For input, first use the pre-trained word2vector Chinese word vector to initialize the input sentence into a matrix, then open a window with a fixed width, and only encode the information in the window.
[0029] A2: The structure of the encoding part specifically includes two different convolutional neural network (CNN) convolution kernels, one is used to detect whether the Chinese characters in the window contain typos, and its width and height are related to the size of the window and the word vector The size is the same, and the output needs to go through a nonlinear transformation function sigmoid function, and the other is used to encode the Chinese character information ...
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