Chinese Named Entity Recognition in the Ship News Field Based on Adversarial Transfer Learning
Pages 562 - 567
Abstract
In natural language processing (NLP), named entity recognition (NER) is a preliminary task that aims to recognize predefined types of named entities from a given text sequence. The accuracy of NER has been significantly increased by deep learning. However, deep learning models rely heavily on annotated data while many specific fields, e.g., the ship news field, often lack of extensive annotated data, which makes the NER task much more challenging. In this paper, we propose a new NER model based on adversarial learning, which is composed of BERT, convolutional neural networks (CNN) and conditional random fields (CRF). In addition, the Chinese word segmentation (CWS) task or the NER task from different domains is introduced to increase the accuracy of NER in the domain through the transfer of task-shared information. Adversarial learning can fully exploit task-shared information and filter out noise. Experiments are conducted on three different public datasets, as well as on our own ship news dataset. According to the results, our model outperforms the state-of-the-art baselines and achieves good performance in the ship news field.
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Index Terms
- Chinese Named Entity Recognition in the Ship News Field Based on Adversarial Transfer Learning
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February 2024
757 pages
ISBN:9798400709234
DOI:10.1145/3651671
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Published: 07 June 2024
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ICMLC 2024
ICMLC 2024: 2024 16th International Conference on Machine Learning and Computing
February 2 - 5, 2024
Shenzhen, China
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