Model training method and text information processing method, system and device and storage medium

Abstract

The invention discloses a training method of a text information processing model, a text information processing method, system and device and a storage medium, which can be applied to the field of artificial intelligence; the training method comprises the following steps: coding a first training text to obtain first semantic information; encoding the first candidate word to obtain second semantic information; predicting classification of the first training text according to the first semantic information to obtain a first classification result; predicting the weight of the first candidate word according to the first semantic information and the second semantic information; determining a first loss value according to the first classification result and the type label of the first training text; determining a second loss value according to the weight of the first candidate word and the weight label of the first candidate word; and training the text information processing model according to the first loss value and the second loss value. According to the method, the training cost can be reduced or higher model precision can be obtained under the condition of the same training cost.

Description

technical field [0001] The present application relates to artificial intelligence technology, especially a text information processing model training method, text information processing method, system, device and storage medium. Background technique [0002] At present, word weighting tasks are mostly completed in two ways: statistics and text classification. The statistics are unsupervised, and the representative method is TF-IDF (term frequency–inverse document frequency, a commonly used weighting technology for information retrieval and data mining. ), mutual information MI (mutual information, is a commonly used information measure in information theory). There is a supervised method, represented by deep learning. Compared with the statistical method, the deep learning model can learn the relationship between keywords and sentences, so it is more accurate in the task of keyword extraction. [0003] However, training a deep learning model requires a large number of train...

AI Technical Summary

Problems solved by technology

[0003] However, training a deep learning model requires a large number of training samples. These training samples need to be labeled manually. For keyword labeling, it is difficult and the labeling efficiency is relatively low, so the acquisition cost is high

The same words have different key degrees in different contexts. Therefore, if you need to obtain a model that can accurately predict the weight of words in sentences in different contexts, you need to use a large number of training samples, and the cost comparison high

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