A method and system for understanding semantically distributed text with adaptive architecture

Abstract

The present invention relates to a text understanding method and system of self-adaptive architecture semantic distribution. The main modules included in the self-adaptive architecture semantic distribution text understanding system include a text basic encoder, a semantic association key distribution representation module, and an architecture self-adaptive classification network module. The present invention utilizes the context multi-head attention semantic distribution representation in the semantic association key distribution representation module, which can better utilize the context information and eliminate the ambiguity of the context expression; meanwhile, through the key information multi-head attention semantic distribution representation, keywords, Phrases and other key information to solve the problem that key information in subdivided fields is sensitive to classification tasks. The architecture-adaptive classification network module can adaptively select the classification network layer according to the similarity of classification categories, so that similar classifications share weight parameters, and classifications with large differences in classification categories do not share weights, so that classification can be better solved. A problem where categories cannot be completely independent.

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...

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Problems solved by technology

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

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