Short text classification method based on topic word vectors and convolutional neural network

Abstract

The invention discloses a short text classification method based on a topic word vector and a convolutional neural network, which comprises the following steps: 1) a data acquisition stage: acquiringshort text data according to requirements, and labeling the short text data as a training set; 2) a data preprocessing stage: performing word segmentation, stop word removal, useless text filtering and the like on the text; 3) representing short text features, namely respectively representing a theme level and a word vector level; 4) carrying out subject term vector joint training; 5) optimizing and iterating parameters of the convolutional neural network classification model; and 6) performing category prediction on the new sample. According to the invention, short text data characteristics are combined; in the feature representation stage, a topic vector and a word vector are combined for representation; semantic feature expansion is carried out on the data characteristics of the short text, text semantic information is further mined by utilizing the local sensitive information extraction capability of the convolutional neural network in the classification model training stage, and indexes such as short text classification task category prediction accuracy can be improved.

Description

technical field [0001] The invention relates to the field of text classification, in particular to a short text classification method based on subject word vectors and convolutional neural networks. Background technique [0002] With the emergence of large-scale text information on the Internet, more manpower and material resources are required to effectively mine and utilize massive text information. Text classification tasks have become an important method of processing text data and an important means of managing text corpus. . Text classification is one of the main research areas of natural language processing (NLP). The text classification task can be understood as the process of extracting the article mapping into the set label set by analyzing the structural features and semantic information of the text. [0003] With the popularity and explosive growth of real-time new applications such as online communication, news alerts, e-commerce, social media, and online ques...

AI Technical Summary

Problems solved by technology

[0004] Purpose of the invention: The technical problem mainly solved by the present invention is to solve the problem of poor classification effect due to the short text length, insufficient semantic information, and sparse word co-occurrence data characteristics

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