<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Because, the existing named entity recognition models lack the Specificity of the field, and most of their combine the prediction of entity Location and entity category, which results in the accumulation of errors. So, named entity recognition model based on multi-task learning and pointer network is proposed, and innovations are made in the task Construction and domain entity information utilization in the model of Named entity recognition. This model is based on Transformer with multi-Head attention mechanism, and decomposes traditional tasks in entity recognition tasks and entity classification tasks, and carries out multi-tasks Task learning to reduce the accumulation of errors between tasks. Model in this paper also uses the similarity calculation based on the Comprehensive description of the entity category in entity classification Task for better pertinence of the domain entity. Experiments are conducted on public datasets and domain datasets to prove the advancement of the Model.</p>
</abstract>

Because, the existing named entity recognition models lack the Specificity of the field, and most of their combine the prediction of entity Location and entity category, which results in the accumulation of errors. So, named entity recognition model based on multi-task learning and pointer network is proposed, and innovations are made in the task Construction and domain entity information utilization in the model of Named entity recognition. This model is based on Transformer with multi-Head attention mechanism, and decomposes traditional tasks in entity recognition tasks and entity classification tasks, and carries out multi-tasks Task learning to reduce the accumulation of errors between tasks. Model in this paper also uses the similarity calculation based on the Comprehensive description of the entity category in entity classification Task for better pertinence of the domain entity. Experiments are conducted on public datasets and domain datasets to prove the advancement of the Model.

A Named Entity Recognition Model Based on Multi-Task Learning and Cascading Pointer Network

School of Computer Science and Engineering

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Because, the existing named entity recognition models lack the Specificity of the field, and most of their combine the prediction of entity Location and entity...

A Named Entity Recognition Model Based on Multi-Task Learning and Cascading Pointer Network

Online veröffentlicht: 24. Mai 2023

Seitenbereich: 52 - 62

DOI: https://doi.org/10.2478/ijanmc-2022-0027

Schlüsselwörter
Named Entity Recognition, Multi-Tasks Task Learning, Transformer

© 2022 Chaoyang Geng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A Named Entity Recognition Model Based on Multi-Task Learning and Cascading Pointer Network

Online veröffentlicht: 24. Mai 2023

Seitenbereich: 52 - 62

DOI: https://doi.org/10.2478/ijanmc-2022-0027

SchlüsselwörterNamed Entity Recognition, Multi-Tasks Task Learning, Transformer

© 2022 Chaoyang Geng et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Named Entity Recognition, Multi-Tasks Task Learning, Transformer