Word definition generation method based on recurrent neural network and latent variable structure
A technology of cyclic neural network and latent variables, applied in the field of natural language processing, can solve problems such as polysemy of a word, and achieve high-quality and easy-to-understand effects
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[0070] figure 2 It is a schematic diagram of the model structure of the word definition generation method based on the cyclic neural network and the latent variable structure of the present invention. This implementation includes a context semantic extractor, a paraphrase variational autoencoder, and a paraphrase generation decoder.
[0071] Some basic concepts and interrelationships involved in the present invention
[0072] 1. Vocabulary: It consists of all the words included in the dictionary, that is, it consists of all defined words;
[0073] 2. Initial vocabulary: count the first 70,000 characters with the highest frequency in the WikiText-103 dataset, remove special symbols, and only keep English words as the initial vocabulary;
[0074] 3. Basic corpus: It is to organize the language materials that have actually appeared together to form a corpus, so that when explaining words, we can draw materials from them or obtain data evidence. The corpus described in the pres...
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