Artificial intelligence-based multi-label classification method and system of multi-level text

Abstract

The invention relates to an artificial intelligence-based multi-label classification method and system of multi-level text. The method includes: 1) utilizing a neural network to construct a multi-label classification model of the multi-level text, and obtaining text class prediction results of training text according to the model; 2) carrying out learning on parameters of the multi-label classification model of the multi-level text according to existing text class labeling information in the training text and the text class prediction results, which are of the training text and are obtained inthe step 1), to obtain a multi-label classification model of the multi-level text with determined parameters; and 3) utilizing the multi-label classification model of the multi-level text with the determined parameters to classify to-be-classified text. The method infers labels of the formed text simply through the document-level labeling information, and can be well applied to scenes where labels of formed text are difficult to collect; compared with traditional multi-instance learning (MIL) methods, the method of the invention introduces minimal assumptions, and can better fit actual data;and the method of the invention has good scalability.

Description

technical field [0001] The present invention relates to the fields of artificial intelligence, text classification, and content visualization, in particular to an artificial intelligence-based multi-level text multi-label classification method and system. Background technique [0002] The understanding and analysis of text content is the research goal of natural language processing. Most of the text content exists in the form of documents, and each document corresponds to a file. Typical file formats include TXT, HMTL, WORD, PDF, etc. At present, with the vigorous development of the Internet and mobile Internet, the number of documents to be analyzed has increased sharply. How to classify texts with different granularities (such as sentences, paragraphs, and documents) is of great significance to information discovery, information browsing, and analysis. For example, a large number of e-commerce websites provide user comments, such as "This juicer is very delicate for baby...

AI Technical Summary

Problems solved by technology

The problem faced by the first method is that the category labeling information of fine-grained text is very little, the collection cost is high, and large-scale application is unrealistic; the problem faced by the second method is that the assumptions in the traditional multi-instance learning method are too strong and cannot be well modeling actual data

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