Text keyword generating method and device and electronic device and readable storage medium

Abstract

One embodiment of the invention provides a text keyword generating method and device and an electronic device and a readable storage medium, which can be applied in the technical field of multimedia. The method comprises following steps: obtaining a text to be detected, performing vector representation on each character in the text to be detected to obtain a character matrix of the text to be detected; inputting the character matrix of the text to be detected into a pre-established keyword model to obtain a keyword matrix corresponding to the character matrix of the text to be detected; calculating the similarity between each vector in the keyword matrix and a character vector in the vector model, and obtaining a keyword of the text to be detected according to the similarity. The keyword model is obtained by training the training data through long and short term memory LSTM neural network. The training data comprises: a character matrix for a sample text and a keyword matrix of keywords in the sample text corresponding to the character matrix. The embodiment of the present invention improves the accuracy of text keyword generation.

Description

technical field [0001] The invention relates to the field of multimedia technology, in particular to a method and device for generating text keywords, electronic equipment and a readable storage medium. Background technique [0002] With the development of the Internet, Internet users are exposed to more and more information every day. Whether it is news or movie subtitles, how to extract useful information for Internet users from massive amounts of information is becoming more and more important. At the same time, extracting keywords of information is very helpful for Internet users to quickly understand what the author of the information wants to express. At present, commonly used keyword extraction algorithms include: TF-IDF (term frequency–inverse document frequency, term frequency-inverse document frequency) algorithm and Textrank algorithm. [0003] Among them, the TF-IDF algorithm judges whether a word is important in the text through word frequency, and at the same ...

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Problems solved by technology

[0006] However, the TF-IDF algorithm only mines information from the perspective of word frequency, and cannot reflect th

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