Text classification method for open network questions in specific field

Abstract

The invention belongs to the technical field of text classification processing, and particularly relates to a text classification method for open network questions in a specific field. According to the method, the problems of lack of enough available corpus sets with category marks, low network text information amount and high noise under the condition of executing network open text classificationtasks in certain specific fields are solved, and a new method is provided for hierarchical classification of open network questions in the fields. According to the method, open network questions andwritten texts in a specific domain are utilized to enable word embedding representation in the domain to better conform to domain knowledge features, and meanwhile, a semi-supervised method is used for accelerating classification model training and reducing required marked samples; and in addition, category classification at a multi-granularity level is realized in combination with conditional probability. The method can assist the extraction, discrimination and construction of data in such fields as question and answer system, emotion analysis and domain knowledge bases and the like.

Description

technical field [0001] The invention belongs to the technical field of text classification processing, and in particular relates to a text classification method for open network questions in specific fields. Background technique [0002] The multiple intelligences of human beings are closely related to language. Human logical thinking is in the form of language, and most of human knowledge is also recorded and handed down in the form of language. Therefore, it is also an important, even core part of artificial intelligence. From the beginning of artificial intelligence research, people have been looking for ways for machines to understand the world. Among them, Text Classification is a widely used subject in the field of natural language processing. The specific description of the text classification task is to use the computer to automatically classify and mark the text set according to a certain classification system or standard. Since the Internet developed at an asto...

AI Technical Summary

Problems solved by technology

However, the text classification methods of most current neural network models have the following problems: 1) the amount of data required for the learning of neural network model classifiers is large, and a large number of labeled training samples are required, and the cost is relatively high

Indeed, deep convolutional models are derived from deep models originally developed for image processing, but new deep architectures for text processing may challenge this conclusion in the near future

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