Baidu Online Network Technology (beijing) Co., Ltd.

Weishan Dong

Yuan Xia

Wei Fan

Jingbo Zhou

Hongliang Fei

Chaochun Liu

The present disclosure provides a method and apparatus for building a text classification model, and a text classification method and apparatus. The method of building a text classification model comprises: obtaining a training sample; obtaining a vector matrix corresponding to the text, after performing word segmentation for the text based on an entity dictionary; using the vector matrix corresponding to the text and a class of the text to train a first classification model and a second classification model respectively; during the training process, using a loss function of the first classification model and a loss function of the second classification model to obtain a loss function of the text classification model, and using the loss function of the text classification model to adjust parameters for the first classification model and the second classification model, to obtain the text classification model formed by the first classification model and the second classification model. The text classification method comprises: obtaining a to-be-classified text; obtaining a vector matrix corresponding to the text, after performing word segmentation for the text based on an entity dictionary; inputting the vector matrix into a text classification model, and obtaining a classification result of the text according to output of the text classification model. The text classification effect can be improved through the technical solutions of the present disclosure.

Method and apparatus for building text classification model, and text classification method and apparatus

A method and apparatus for outputting information are provided. A specific embodiment of the method comprises: retrieving at least one medical entity keyword and at least one attribute keyword from a target medical text; then generating a set of keyword pairs, each of the keyword pairs including the retrieved medical entity keyword and the retrieved attribute keyword; then retrieving, for each of the keyword pairs in the set of keyword pairs, a text characteristic of the each of the keyword pairs in the target medical text, and introducing the retrieved text characteristic into a pre-trained association-relationship determination model to obtain an association result corresponding to the each of the keyword pairs; and finally outputting the keyword pairs having the association relationship in the set of keyword pairs. The embodiment improves the abundance of outputted information contents.

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Beijing, China

Weishan Dong