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Depression detection method based on knowledge distillation and emotion integration model

A technology that integrates models and detection methods. It is used in psychotherapy, character and pattern recognition, and unstructured text data retrieval. It can solve problems such as inability to handle new users and users with sparse behavior.

Pending Publication Date: 2021-07-13
重庆心暖舟科技有限责任公司
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Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that the social media-based depression detection method requires users to have enough posting content and behaviors on social media, and cannot deal with new users and users with sparse behaviors, and proposes a method based on knowledge distillation Depression detection method with integrated model of emotion

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  • Depression detection method based on knowledge distillation and emotion integration model
  • Depression detection method based on knowledge distillation and emotion integration model
  • Depression detection method based on knowledge distillation and emotion integration model

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Embodiment 1

[0046] Example 1, such as Figure 1-3 As shown, the present invention provides a method for detecting depression based on knowledge distillation and emotional integration model, comprising the following steps:

[0047] (1) Preprocess the answer texts of several depression-related questions and external data sets;

[0048] (2), based on step (1) preprocessed microblog text and pre-trained language model training emotion classification model;

[0049] Use the Chinese pre-trained BERT language model to predict the emotional probability vector, use softmax to normalize the probability vector, and calculate the loss function for model optimization.

[0050] Specifically, as figure 2As shown, in this step, a BERT-Base model with a 12-layer Transformer is used to process the input preprocessed text data, and obtain the hidden representation of each word, which is denoted as H=[h 1 , h 2 ,...,h M ], where M is the text length. Next, an attention network is used to convert this s...

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Abstract

The invention provides a depression detection method based on knowledge distillation and an emotion integration model, and relates to the technical field of depression models, and the method comprises the following steps: preprocessing answer texts of a plurality of depression-related questions and an external data set, wherein the data set is a microblog data set, and the microblog data set obtains user information according to questions and answers filled in a microblog by a user, so that a preprocessed microblog text and a text of question answering of the user are obtained; training an emotion classification model based on the preprocessed microblog text and a pre-training language model, wherein the pre-training language model is a Chinese pre-training BERT language model, and the Chinese pre-training BERT language model is used for predicting an emotion probability vector. According to the invention, the problems that the depression detection method based on the social media requires the user to have enough published content and behaviors on the social media and cannot process new users and users with sparse behaviors are solved.

Description

technical field [0001] The invention relates to the technical field of depression models, in particular to a method for detecting depression based on knowledge distillation and emotion integration models. Background technique [0002] With the rapid development of modern society, people's life pressure is increasing. Many people suffer from depression due to work, family and economic pressure in urban life. Depression is a very destructive mental disorder. People suffering from depression lose interest in life and even lose their desire to live. According to incomplete statistics, the number of depressed patients in the country has reached 300 million, and nearly 50 million people suffer from depression in China alone. [0003] With more and more patients suffering from depression, the detection mechanism of depression is still lagging behind. The existing depression detection methods mainly include methods based on questionnaires, methods based on social media, and methods ...

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Application Information

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IPC IPC(8): G06F16/33G06F16/35G06F16/36G06K9/62G16H20/70
CPCG06F16/3344G16H20/70G06F16/367G06F16/35G06F18/2411G06F18/2415G06F18/214
Inventor 武楚涵张艳
Owner 重庆心暖舟科技有限责任公司
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