Title entity recognition model training method, title entity recognition method and device

Abstract

The embodiment of the invention discloses a title entity recognition model training method and device and a title entity recognition method and device. A specific embodiment of the method comprises the steps of training an initial mask text recognition model based on an article information sample group to obtain a trained mask text recognition model, the initial mask text recognition model comprising a pre-trained initial text coding model, and the pre-trained initial text coding model comprising an item information sample group; the trained mask text recognition model comprises a trained text coding model; determining the trained text coding model and a preset decoding network as an initial title entity recognition model; and training the initial title entity recognition model based on the title sample group to obtain a trained title entity recognition model. According to the embodiment, the marking time is shortened, and the recognition accuracy and robustness of the title entity recognition model are improved.

Description

technical field [0001] The embodiments of the present disclosure relate to the field of computer technology, and specifically relate to a title entity recognition model training method, a title entity recognition method and a device. Background technique [0002] The named entity recognition model can quickly identify entity information in information, and is widely used in the field of intelligent search. At present, the training of named entity recognition models usually adopts the following method: a large amount of named entity data with entity labels needs to be constructed for training to improve accuracy and robustness. [0003] However, the above method usually has the following technical problems: as more and more unlabeled named entity samples are obtained, the difficulty of labeling entity labels becomes more and more difficult, and the labeling time is longer; in addition, through a large number of entity labels with entity labels Training on named entity data c...

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Problems solved by technology

[0003] However, the above method usually has the following technical problems: as more and more unlabeled named entity samples are obtained, the difficulty of labeling entity labels becomes more and more difficult, and the labeling time is longer; in addition, through a large number of entity labels with entity labels Training on named entity data cannot accurately identify the semantic relationship between words in the non-entity label corpus, resulting in a low accuracy rate for named entity recognition

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