Method and device for constructing agricultural corpus

Abstract

The invention provides a method and a device for constructing an agricultural corpus. The method comprises the following steps: determining a labeling tool based on a labeling system of the agricultural corpus; performing first labeling training based on the randomly selected agricultural sample data to obtain a machine learning model; performing second labeling training based on the agricultural sample data selected by active learning to obtain a trained and updated machine learning model, and obtaining an updated labeling tool; performing formal labeling based on the agricultural sample data selected by active learning to obtain labeled data; and constructing an agricultural corpus based on the agricultural sample data and the labeled data thereof. Through active learning and labeling consistency analysis, the effect of data labeling is improved, a large amount of standard and effective agricultural labeling data is obtained, and the problem of data chaos of a current agricultural corpus is solved. Meanwhile, machine learning model training achieves a better effect by using fewer sample sets, the consumption of data labeling is reduced, and the effect of agricultural corpus construction is improved.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a method and device for constructing an agricultural corpus. Background technique [0002] Most of the traditional agricultural knowledge bases are compiled manually by domain experts. Although the quality is high, there are problems such as small scale, low coverage, multiple redundancy, and update delays. With the advent of the era of big data, the use of text analysis methods to automatically mine agricultural information from massive semi-structured and unstructured network data has become a practical solution for building and maintaining a knowledge service platform in the agricultural field. However, the types and forms of agricultural knowledge scattered in network data are different, and it is necessary to screen and classify massive data to construct an agricultural knowledge map. However, there is no mature way to construct the agricultural knowledge map. Con...

AI Technical Summary

Problems solved by technology

However, there is no mature way of constructing agricultural knowledge graph

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