Statement classification method and system, electronic equipment, refrigerator and storage medium

Abstract

The invention provides a statement classification method and system, electronic equipment, a refrigerator and a storage medium. The statement classification method comprises the following steps of: obtaining a word vector corresponding to each word in a target statement, wherein the word vector comprises a word meaning vector and a part-of-speech vector; inputting the word vector corresponding to each word in the target statement into a long / short-term memory LSTM model so as to obtain a state matrix of the target statement, wherein the state matrix comprises initial semantic information and word order information corresponding to the target statement; inputting the state matrix into a convolutional neural network CNN model so as to obtain a characteristic quantity corresponding to the target statement; and classifying the target statement according to the characteristic quantity of the target statement. According to the method, the correctness of statement classification and statement type recognition can be improved and the user experience can be enhanced.

Description

technical field [0001] The embodiment of the present invention relates to the field of smart home technology, and in particular to a sentence classification method, system, electronic equipment, refrigerator and storage medium. Background technique [0002] With the popularization and use of smart home appliances and smart phones, people are more and more accustomed to using voice recognition systems or search engines on smart home appliances and smart phones to ask questions or control smart home appliances. Taking the speech recognition system as an example, after the speech is recognized as sentence text, it is necessary to classify the sentence text, and then obtain the corresponding answer or operation instruction according to the type of sentence text. Therefore, the process of sentence classification is to realize intelligent question answering, An important step in language processing tasks such as search engines. [0003] The current sentence text classification me...

AI Technical Summary

Problems solved by technology

[0004] However, when using the convolutional neural network CNN model for sentence classification, because only the abstract features of each word in the sentence can be obtained, the lack of word order features of each word in the sentence causes the problem of inaccurate sentence classification; while using the cyclic neural network When the RNN model classifies sentences, because its ability to abstract the features of each word is not as good as that of CNN, it will also cause the problem of low classification accuracy of subsequent sentences.

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