Text feature extraction method based on semantic analysis

Abstract

The invention provides a text feature extraction method based on semantic analysis. The method comprises the following steps: initializing a text corpus, performing word segmentation and unused word removal processing on texts, clustering the texts according to semantic relevance (the formula is as shown in the specification), constructing a lexical semantic network model graph, obtaining the status and the contribution of a vocabulary in the whole text according to the importance of the vocabulary in a lexical semantic network model, setting a proper importance threshold, and extracting a feature vocabulary vector of the text. Compared with the tradition word frequency-inverse document frequency method, the text feature extraction method provided by the invention has the advantages of having higher accuracy, overcoming the defect that only one category of text features can be extracted by the information gain method, having higher application value, accurately calculating the contribution of different vocabularies to the idea of the text and meanwhile providing good theoretical basis for subsequent text clustering.

Description

technical field [0001] The invention relates to the technical field of semantic network, in particular to a text feature extraction method based on semantic analysis. Background technique [0002] Currently commonly used text feature extraction methods include word frequency-inverse document frequency method—TF-IDF, information gain method, mutual information and other methods; the simple structure of TF-IDF cannot effectively reflect the importance of words or phrases and the characteristics of feature values. Distribution, so the accuracy of TF-IDF is not very high. The information gain method is only suitable for extracting text features of one category, but cannot be used for extracting text features of multiple categories. The mutual information method considers the ratio of the probability of category occurrence to the probability of collection, which will cause a defect, that is, the difference in the number of texts in the category collection will greatly affect the...

AI Technical Summary

Problems solved by technology

However, the commonly used text feature extraction methods do not consider the semantic status of the feature vocabulary and its contribution to the expression of the text.

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