Model training method and device, knowledge classification method and device, equipment and medium

Abstract

The embodiment of the invention provides a model training method and device, a knowledge classification method and device, equipment and a medium, and belongs to the technical field of machine learning. The method comprises the following steps: acquiring original annotation data; the original annotation data comprises question stem data, option data and answer data; performing coding processing on the question stem data to obtain a question stem representation vector; encoding the option data and the answer data according to a preset knowledge graph to obtain an option attribute value and an answer attribute value; performing word segmentation and splicing processing on the option attribute value and the answer attribute value to obtain an option answer representation vector; performing vector splicing on the question stem representation vector and the option answer representation vector to obtain question data; a preset pre-training model is trained according to the question data, a knowledge classification model is obtained, the knowledge classification model is used for conducting knowledge classification on the target question, and the knowledge classification model obtained by the embodiment of the invention can improve the accuracy and efficiency of knowledge classification.

Description

technical field [0001] Embodiments of the present disclosure relate to the technical field of machine learning, and in particular to a model training method, a knowledge classification method, a device, a device, and a medium. Background technique [0002] With the development of artificial intelligence technology, there are currently technical solutions that can process various data based on artificial intelligence technology. For example, machine reading comprehension technology can be used to give answers to questions. Machine reading comprehension is a technology that enables machines to understand natural language texts and answer corresponding answers given questions and documents. This technology can be applied in many fields such as text question answering, information extraction in knowledge graph and event graph, and dialogue system. [0003] However, in some application scenarios, there is a lack of technical solutions for classifying knowledge. For example, in t...

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Problems solved by technology

[0003] However, in some application scenarios, there is a lack of technical solutions for classifying knowledge. For example, in an English online education scenario, it is necessary to classify the topics for examining relevant English knowledge points, so as to divide the topics of the same knowledge points and conduct special training for users.

Since the number of English questions is too large, and some new questions are developed every year; if each question is divided manually, the workload is heavy, the efficiency is low, and error-prone

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