Method and device for determining multi-level classification labels of articles

Abstract

The invention discloses a method and a device for determining a multi-level classification label of an article, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: acquiring a sample article, and acquiring each level of sample classification label corresponding to the sample article according to a predefined article level classification standard; training the sample articles and all levels of sample classification labels corresponding to the sample articles to obtain a multi-level text classification model; and obtaining a target article, and determining a multi-level target classification label corresponding to the target article by using the multi-level text classification model. According to the embodiment, the multi-level text classification model is constructed by using the sample articles and the sample classification labels of all levels of the sample articles, hidden information between categories can be fully mined, then the multi-level classification labels of the articles are determined based on the constructed multi-level text classification model, and the accuracy of article classification is improved.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a method and device for determining multi-level classification labels of items. Background technique [0002] Multilevel classification refers to a multi-classification problem in which the category itself has a hierarchy. For example, organisms are divided into animals and plants, animals are divided into chordates and non-chordates, and plants are divided into bryophytes and ferns. The multi-level classification of items is a kind of multi-level classification problem. Accurately providing multi-level classification labels of items can improve the efficiency of items entering and leaving the warehouse, and can also bring a good shopping experience to users. [0003] For the multi-level classification of items, there are currently two solutions: using a conventional machine learning model, assuming that there is no relationship between categories, regardless of the closeness r...

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Problems solved by technology

[0004] In the process of realizing the present invention, the inventor found that there are at least the following problems in the prior art: when the conventional machine learning model solves the multi-level classification problem, the classification effect is relatively poor due to the inability to fully mine the hidden information between classes; the traditional cascade model In , when each level of classification has many subclasses and the classification level is deep, it is necessary to build a large number of subclassifiers, which will have a catastrophic impact on model development

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