POISE: Efficient Cross-Domain Chinese Named Entity Recognization via Transfer Learning

Sheng, Jiabao; Wumaier, Aishan; Li, Zhe

doi:10.3390/sym12101673

Cited by 6 publications

(5 citation statements)

References 30 publications

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“…In order to evaluate the performance of NEFTL-Boud model in migrating data to complete the task of named entity recognition, experiments will be compared with the following four Chinese NER methods or models based on migration learning: (1) POISE [27]. (2) NER-CWS [24].…”

Section: Baseline Comparison Methodsmentioning

confidence: 99%

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A Research Toward Chinese Named Entity Recognition Based on Transfer Learning

Kang

Xiao

Zhang

et al. 2023

Int J Comput Intell Syst

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To improve the performance of named entity recognition in the lack of well-annotated entity data, a transfer learning-based Chinese named entity recognition model is proposed in this paper. The specific tasks are as follows: (1) first/, a data transfer method based on entity features is proposed. By calculating the similarity of feature distribution between low resource data and high resource data, the most representative entity features are selected for feature transfer mapping, and the distance of entity distribution between the two domains is calculated to make up the gap between the data of the two domains then model is trained by high resource data. (2) Then, an entity boundary detection method is proposed. This method utilizes the BiLSTM+CRF as the main structure and integrates character boundary information to assist the attention network to improve the model’s ability to recognize entity boundaries. (3) Finally, multiple named entity recognition methods are selected as baseline methods for comparison, and experiments are conducted on several datasets. The results show that the model proposed in this paper improves the accuracy of named entity recognition by 1%, the recall rate by 2%, and the F1 value by 2% on average in the field with low-resource.

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Section: Baseline Comparison Methodsmentioning

confidence: 99%

“…1. ACL paper data set: this data set is the resume data collected in the paper ACL 2018 Chinese NER using Lattice LSTM [27], and the format of the data is shown in Table 4. Each line is composed of a word and its corresponding labels, with tabs as separators, and sentences are separated by a blank line.…”

Section: Datasetmentioning

confidence: 99%

A Research Toward Chinese Named Entity Recognition Based on Transfer Learning

Kang

Xiao

Zhang

et al. 2023

Int J Comput Intell Syst

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show abstract

“…B is the beginning of the entity, I is the non-beginning part of the entity and O is the non-entity part. There are seven types of tags to be predicted, namely, I-PER, I-ORG, I-LOC, B-PER, B-ORG, B-LOC and O [34].…”

Section: Labeling Methods and Model Evaluation Indexmentioning

confidence: 99%

Low-Resource Named Entity Recognition via the Pre-Training Model

Chen

Pei

et al. 2021

Symmetry

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Named entity recognition (NER) is an important task in the processing of natural language, which needs to determine entity boundaries and classify them into pre-defined categories. For low-resource languages, most state-of-the-art systems require tens of thousands of annotated sentences to obtain high performance. However, there is minimal annotated data available about Uyghur and Hungarian (UH languages) NER tasks. There are also specificities in each task—differences in words and word order across languages make it a challenging problem. In this paper, we present an effective solution to providing a meaningful and easy-to-use feature extractor for named entity recognition tasks: fine-tuning the pre-trained language model. Therefore, we propose a fine-tuning method for a low-resource language model, which constructs a fine-tuning dataset through data augmentation; then the dataset of a high-resource language is added; and finally the cross-language pre-trained model is fine-tuned on this dataset. In addition, we propose an attention-based fine-tuning strategy that uses symmetry to better select relevant semantic and syntactic information from pre-trained language models and apply these symmetry features to name entity recognition tasks. We evaluated our approach on Uyghur and Hungarian datasets, which showed wonderful performance compared to some strong baselines. We close with an overview of the available resources for named entity recognition and some of the open research questions.

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“…Therefore, transfer learning is proposed to bridge this gap. Transfer learning involves using pre-trained models from images in other domains and fine-tuning the parameters of the model on the target dataset to make it applicable to the target task [19,20]. This process is widely used in the field of medical image analysis [21].…”

Section: Introductionmentioning

confidence: 99%

Radiomics and Its Feature Selection: A Review

Zhang,

Guo,

Jin

2023

Symmetry

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Medical imaging plays an indispensable role in evaluating, predicting, and monitoring a range of medical conditions. Radiomics, a specialized branch of medical imaging, utilizes quantitative features extracted from medical images to describe underlying pathologies, genetic information, and prognostic indicators. The integration of radiomics with artificial intelligence presents innovative avenues for cancer diagnosis, prognosis evaluation, and therapeutic choices. In the context of oncology, radiomics offers significant potential. Feature selection emerges as a pivotal step, enhancing the clinical utility and precision of radiomics. It achieves this by purging superfluous and unrelated features, thereby augmenting model performance and generalizability. The goal of this review is to assess the fundamental radiomics process and the progress of feature selection methods, explore their applications and challenges in cancer research, and provide theoretical and methodological support for future investigations. Through an extensive literature survey, articles pertinent to radiomics and feature selection were garnered, synthesized, and appraised. The paper provides detailed descriptions of how radiomics is applied and challenged in different cancer types and their various stages. The review also offers comparative insights into various feature selection strategies, including filtering, packing, and embedding methodologies. Conclusively, the paper broaches the limitations and prospective trajectories of radiomics.

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POISE: Efficient Cross-Domain Chinese Named Entity Recognization via Transfer Learning

Cited by 6 publications

References 30 publications

A Research Toward Chinese Named Entity Recognition Based on Transfer Learning

A Research Toward Chinese Named Entity Recognition Based on Transfer Learning

Low-Resource Named Entity Recognition via the Pre-Training Model

Radiomics and Its Feature Selection: A Review

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