Text keyword extracting method based on subject model

Abstract

The invention discloses a text keyword extracting method based on a subject model. The method comprises the following steps: firstly obtaining a probability matrix WT of the lexical item and the subject of a training text set between the lexical item and the subject obtained through training by the subject model from a great deal of text training sets by using the subject model method ; further obtaining a probability matrix B of the lexical item and the subject of candidate keyword composed of the set of probability vectors of the subject and the lexical item in a candidate keyword set A, and obtaining a word frequency weight vector D of the candidate keyword corresponding to the candidate keyword set, cyclically computing by using the probability matrix B of the subject of the candidate keyword through the weight vector of the lexical item of the candidate keyword and the subject vector of the text to obtain the finally modified text subject vector and lexical item weight proportion vector, and thus extracting the keyword of the text. According to the text keyword extracting method based on the subject model, the error in keyword extraction due to different lengths of texts is reduced, and the keyword more proper to represent the text content is extracted.

Description

technical field [0001] The present invention relates to a method for extracting keywords from text, more specifically, relates to a method based on obtaining a probability matrix between terms and topics from a topic model, and then using the matrix to extract more expressive text from the text The method of subject content keywords. Background technique [0002] Before using the computer to process the text, it is necessary to formally represent the text. In traditional methods, keywords are usually extracted from the text to represent the content of the text. Key words are extracted from the text, and the word frequency of the key words is used as a very important basis. However, since the lengths of different types of texts are different, there will be large errors in the word frequency of keywords. Especially for short texts, many terms in the short text appear only once. Under the above circumstances, if the word frequency of the term is used as the basis for extrac...

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Problems solved by technology

Under the above circumstances, if the word frequency of the term is used as the basis for extracting keywords from the text, it is bound to be difficult to obtain better results

Moreover, the traditional text keyword extraction meth

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