Method and device for classifying sentences based on TF-IDF algorithm and computer storage medium

A TF-IDF, sentence technology, applied in computing, text database clustering/classification, special data processing applications, etc., can solve the problem of inaccurate sentences in news reports

Pending Publication Date: 2021-08-10
CAPITAL NORMAL UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] This application provides a sentence-based TF-IDF feature and preset topic categories to classify n

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  • Method and device for classifying sentences based on TF-IDF algorithm and computer storage medium
  • Method and device for classifying sentences based on TF-IDF algorithm and computer storage medium
  • Method and device for classifying sentences based on TF-IDF algorithm and computer storage medium

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Embodiment Construction

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of this application.

[0044] see figure 1 , figure 1 It is a flow chart of the method for classifying sentences based on TF-IDF provided by the embodiment of the present application, such as figure 1 shown, including the following steps:

[0045] Step S101. Acquire news battle report sentences, and preprocess the sentences.

[0046] In the embodiment of the present application, the news battle report sentence is obtained from the existing news battle report articles; wherein the news battle rep

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Abstract

The invention provides a method and a device for classifying sentences based on TF-IDF (term frequency-inverse document frequency). The method comprises the following steps: obtaining news wartime sentences, and preprocessing the sentences; extracting TF-IDF (term frequency-inverse document frequency) features of the preprocessed sentences; and classifying the news combat sentences based on the TF-IDF features of the sentences and a preset theme category. In this way, the sentences are preprocessed, and the sentences are classified according to the TF-IDF features and the preset theme categories, so that the classification accuracy is improved.

Description

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Claims

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Application Information

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Owner CAPITAL NORMAL UNIVERSITY
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