Text classification method and device, equipment and medium
A text classification and text technology, applied in text database clustering/classification, unstructured text data retrieval, special data processing applications, etc., can solve the problem of high labor cost, insufficient sensitivity of semantics, and difficult to meet the application requirements of complex scene text classification To achieve the effect of maximizing model contribution, improving semantic sensitivity, and effectively balancing classification results
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Embodiment 1
[0076] figure 1 It is a flow chart of a text classification method in Embodiment 1 of the present application. This embodiment of the present application is applicable to classifying and identifying the text to be classified to determine the category of the text to be classified. The method is executed by a text classification device. The device is implemented by software and / or hardware, and is specifically configured in electronic equipment with certain data computing capabilities.
[0077] Such as figure 1 A text classification method shown, including:
[0078] S101. Obtain text to be classified.
[0079] Among them, the text to be classified can be pre-stored locally in the electronic device, in other storage devices associated with the electronic device, or in the cloud, and the text to be classified can be obtained when needed; or the text to be classified can be obtained from the application software that generates the text to be classified Real-time or timing acquis...
Embodiment 2
[0097] figure 2It is a flow chart of a text classification method in Embodiment 2 of the present application. This embodiment of the present application is optimized and improved on the basis of the technical solutions of the foregoing embodiments.
[0098] Further, before "according to the word vector sequence and entity vector sequence, classify and identify the text to be classified", add "input the word vector sequence into the word vector attention mechanism model to determine the attention of each word vector Weight; input the entity vector sequence into the entity vector attention mechanism model to determine the attention weight of each entity vector"; correspondingly, the operation "according to the word vector sequence and entity vector sequence, classify the text to be classified Recognition" is refined as "according to the word vector sequence, entity vector sequence and their respective attention weights, classify and identify the text to be classified", so as to...
Embodiment 3
[0111] image 3 It is a flow chart of a text classification method in Embodiment 3 of the present application. This embodiment of the present application is optimized and improved on the basis of the technical solutions of the foregoing embodiments.
[0112] The operation of "model training on the entity vector coding model" will be described in detail, and the training process of the entity vector coding model will be refined into "based on the entity description text in the entity knowledge graph database as the training sample of the entity; using the described Entity training samples to train the entity vector encoding model" to improve the model training mechanism of the entity vector encoding model.
[0113] Such as image 3 A text classification method shown, including:
[0114] S301. Using the entity description text in the entity knowledge graph database as an entity training sample.
[0115] Wherein, the training samples may include positive training samples and n...
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