A Boundary Assembling Method for Nested Biomedical Named Entity Recognition

Chen, Yanping; Hu, Ying; Li, Yijing; Huang, Ruizhang; Qin, Yongbin; Wu, Yuefei; Zheng, Qinghua; Chen, Ping

doi:10.1109/access.2020.3040182

Cited by 10 publications

(11 citation statements)

References 42 publications

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“…In addition, they have proposed a token-interaction tagger to determine the internal connection among the tokens present within the boundary. There are a number of other neural-network-based approaches that have obtained good results using complex architecture to solve the problem of nested named-entity recognition, such as those presented in [18][19][20], and many more.…”

Section: Related Workmentioning

confidence: 99%

BERT-Based Transfer-Learning Approach for Nested Named-Entity Recognition Using Joint Labeling

et al. 2022

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Named-entity recognition (NER) is one of the primary components in various natural language processing tasks such as relation extraction, information retrieval, question answering, etc. The majority of the research work deals with flat entities. However, it was observed that the entities were often embedded within other entities. Most of the current state-of-the-art models deal with the problem of embedded/nested entity recognition with very complex neural network architectures. In this research work, we proposed to solve the problem of nested named-entity recognition using the transfer-learning approach. For this purpose, different variants of fine-tuned, pretrained, BERT-based language models were used for the problem using the joint-labeling modeling technique. Two nested named-entity-recognition datasets, i.e., GENIA and GermEval 2014, were used for the experiment, with four and two levels of annotation, respectively. Also, the experiments were performed on the JNLPBA dataset, which has flat annotation. The performance of the above models was measured using F1-score metrics, commonly used as the standard metrics to evaluate the performance of named-entity-recognition models. In addition, the performance of the proposed approach was compared with the conditional random field and the Bi-LSTM-CRF model. It was found that the fine-tuned, pretrained, BERT-based models outperformed the other models significantly without requiring any external resources or feature extraction. The results of the proposed models were compared with various other existing approaches. The best-performing BERT-based model achieved F1-scores of 74.38, 85.29, and 80.68 for the GENIA, GermEval 2014, and JNLPBA datasets, respectively. It was found that the transfer learning (i.e., pretrained BERT models after fine-tuning) based approach for the nested named-entity-recognition task could perform well and is a more generalized approach in comparison to many of the existing approaches.

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Section: Related Workmentioning

confidence: 99%

BERT-Based Transfer-Learning Approach for Nested Named-Entity Recognition Using Joint Labeling

et al. 2022

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“…Guo et al [2] extracted the cause-effect, contentcontainer, and other entity relations from encyclopedia text. Chen et al [8] carried out the biomedical ER to recognize proteins, DNAs, RNAs, and other biomedical entities from domain literature. Eberts and Ulges [9] identified drug and disease entities from medical documents, and extracted their adverse effect relations.…”

Section: Related Work a Automatic Information Extractionmentioning

confidence: 99%

Automatic Information Extraction in the Third-Generation Semiconductor Materials Domain Based on DKNet and MANet

Jiang

Yang

2022

IEEE Access

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The third-generation semiconductor materials (TGSMs) is a frontier scientific domain, where researchers need to consult extensive literature for the entity information on materials, devices, preparation methods, and experimental performances, and sort out the complex relations between them. However, the explosion of relevant papers has far exceeded researchers' reading ability. In this article, the TGSM-field automatic information extraction is conducted based on entity recognition (ER) and relation extraction (RE) techniques. First, the corpora used for ER and RE in this field are created. Second, aiming at the complexity of the entities, a neural network using domain knowledge (DKNet) is proposed to improve ER performance. It uses the keyword sequence of each entity type as prior knowledge, adds a dedicated embedding to encode entity categories, then combines prior knowledge and encoded vectors with the context through a gated information fusion module to assist recognition. As for the indicative word dependence problem of entity relations, a multi-aspect attention-based network model (MANet) is proposed to enhance the attention to relation-indicative words, thereby improving the RE performance. Finally, F1 scores of 74.5 and 85.9 were achieved on the created ER and RE test sets, outperforming other advanced models by 3.4 ~ 10.1, which is the best performance of the TGSM-field automatic information extraction.

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“…Different research studies have been conducted on nested BNER in the literature [11][12][13][14][15][16][17]. A novel technique to solve the problem of nested BNER was proposed by [11] where each sentence was converted into a tree.…”

Section: Related Workmentioning

confidence: 99%

“…Using those syntax and position embedding, the attention module assessed the context representation value which were used to feed a CRF layer to predict the possible class labels. The task of nested NER was divided into three models [15]: Identifying boundaries, assembling candidates, and distinguishing actual NEs. In identifying boundaries model, the tokens were used as input to character-level and word level embedding layers.…”

Section: Related Workmentioning

confidence: 99%