Named entity recognition model training method and named entity recognition method

A named entity recognition and training method technology, applied in the field of named entity recognition and natural language processing, can solve the problems of not considering the characteristics of unlabeled data, poor recognition results, etc., achieve the best generalization ability and improve the recognition effect

Pending Publication Date: 2020-10-02
INST OF COMPUTING TECH CHINESE ACAD OF SCI
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Problems solved by technology

However, for an unlabeled data set to be identified, the characteristics of the unlabeled data are not

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  • Named entity recognition model training method and named entity recognition method
  • Named entity recognition model training method and named entity recognition method
  • Named entity recognition model training method and named entity recognition method

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Embodiment Construction

[0024] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below through specific embodiments in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0025] As mentioned in the background technology section, for an unlabeled data set to be identified, the characteristics of the unlabeled data are not considered when directly using the BERT model for identification, resulting in poor recognition results. The present invention combines transductive learning and self-learning methods, uses the training set to train the BERT-CRF model to obtain the named entity recognition model trained in the current round, and then uses the named entity recognition model trained in the current round to mark the data set to be recognized to obtain the w...

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Abstract

The embodiment of the invention provides a named entity recognition model training method and a named entity recognition method. According to the invention, the method comprises the steps: employing atraining set for training a BERT-CRF model to obtain a named entity recognition model trained in this round; marking the to-be-identified data set by using the named entity identification model trained in this round to obtain a weakly marked to-be-identified data set; selecting one part from the weakly marked to-be-identified data set and taking the part and the initial training set as a new training data set to continuously carry out the next round of training on the named entity identification model; therefore, the named entity recognition model uses the to-be-recognized data set to adjustthe model before recognizing the to-be-recognized data set, so the named entity recognition model has better generalization ability, and finally the recognition effect of the model on the to-be-recognized data set is improved.

Description

technical field [0001] The present invention relates to the technical field of natural language processing, specifically to the technical field of named entity recognition, and more specifically to a named entity recognition model training method and a named entity recognition method. Background technique [0002] Natural language processing is to allow computers to understand human language, so as to better realize the interaction between humans and computing (such as voice assistants, automatic message reply, translation software and other applications and human interaction). Natural language processing typically includes word segmentation, part-of-speech tagging, named entity recognition, and syntax analysis. Named Entity Recognition (NER) is an important part of Natural Language Processing (NLP). Named entity recognition refers to the process of identifying the names or symbols of things with specific meanings in the text. Named entities mainly include person names, pla...

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Application Information

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IPC IPC(8): G06F40/295
CPCG06F40/295
Inventor 郭嘉丰范意兴刘艺菲张儒清程学旗
Owner INST OF COMPUTING TECH CHINESE ACAD OF SCI
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