Self-adaptive architecture semantic distribution text understanding method and self-adaptive architecture semantic distribution text understanding system

Abstract

The invention relates to an adaptive architecture semantic distribution text understanding method and an adaptive architecture semantic distribution text understanding system. The adaptive architecture semantic distribution text understanding system comprises the following main modules: a text basic encoder, a semantic association key distribution representation module and an architecture adaptiveclassification network module. According to the method, context multi-head attention semantic distribution representation in the semantic association key distribution representation module is utilized, so that context information can be better utilized, and the ambiguity problem of context representation is eliminated; meanwhile, key information such as keywords and phrases can be extracted through multi-head attention semantic distribution representation of the key information, and the problem that the key information in the subdivision field is sensitive to classification tasks is solved. And the architecture self-adaptive classification network module can select a classification network layer in a self-adaptive manner according to the similarity of classification categories, so that the similar classifications share weight parameters, the classifications with large classification category differences do not share weights, and the problem that the classification categories cannot becompletely independent can be better solved.

Description

technical field [0001] The invention relates to the field of classification and processing of natural language texts, in particular to a text understanding method and system for adaptive framework semantic distribution. Background technique [0002] In the Internet era of information explosion, any website needs to process a large amount of text data, and it is essential to classify and structure the information, and manual processing has completely failed to meet the efficiency requirements of the Internet age. As a natural language processing The most basic task of text classification is important and indispensable. The automatic classification of text can summarize and concentrate a large amount of information, further structure the text information, save manpower and time costs, increase the attention to target information, and thus improve the speed of information processing and decision-making efficiency. [0003] Common applications of text classification include new...

AI Technical Summary

Problems solved by technology

In this case, classification models such as pre-training tend to perform poorly

Images