Short text classification method based on conditional entropy and convolution neural network

Abstract

The invention discloses a short text classification method based on a conditional entropy and a convolution neural network, which relates to the natural language processing field. Comprises the following steps: S1, collecting short text according to requirements to form a training data set; 2, label that training data set accord to categories; 3, perform word segmentation processing on that traindata set; S4, constructing a word vector model; S5, calculating the conditional entropy of all words; 6, construct a stop word dictionary; S7, removing words that do not conform to the conditions andhave less influence on classification; S8, vectorizing all short texts; S9, establishing a convolution neural network model; S10, inputting the vectorized training data set into the convolution neuralnetwork model; S11, continuous iteration, optimization, and finally get the best effect of short text classifier. The invention realizes the filtering of noise words and the accuracy of filtering.

Description

Technical field [0001] The invention relates to the field of natural language processing, in particular to a short text classification method based on conditional entropy and convolutional neural network, and is suitable for short text classification. Background technique [0002] Natural language processing (NLP) is an important direction in the field of computer science and artificial intelligence. It studies various theories and methods that enable effective communication between humans and computers in natural language. Natural language processing is a science that integrates linguistics, computer science, and mathematics. Therefore, research in this field will involve natural language, which is the language people use daily. The purpose of natural language processing is to allow the computer to ‘understand’ what humans ‘say’ to it, and then to perform certain designated tasks. These tasks include spell checking, keyword search, intent recognition, machine translation, dia...

AI Technical Summary

Problems solved by technology

They all have certain shortcomings: decision tree, if we have more data in the video field, in the decision tree, the result of information gain will be biased towards the video field; SVM is sensitive to missing data; KNN category scores are not normalized, and the amount of calculation Large; in theory, the Naive Bayesian model has a smaller error rate than other classification algorithms, but in practice this is not always the case, because the Naive Bayesian model assumes that the attributes are independent of each other, this assumption is in practice application is often not established

For example, when only naive

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