Chinese Named Entity Recognition Based on B-LSTM Neural Network with Additional Features

Ouyang, Liubo; Tian, Yuan; Tang, Hui; Zhang, Boyun

doi:10.1007/978-3-319-72389-1_22

Cited by 12 publications

(9 citation statements)

References 15 publications

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“…For Chinese named entity recognition, Ouyang et al [13] proposed named entity recognition based on the BI-LSTM neural network with additional features. The experimental results showed that the BI-LSTM with word embedding trained on a large corpus achieved the highest F1 score of 92.47%.…”

Section: Related Workmentioning

confidence: 99%

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Medical Named Entity Extraction from Chinese Resident Admit Notes Using Character and Word Attention-Enhanced Neural Network

Gao

Wang

Wang³

et al. 2020

IJERPH

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The resident admit notes (RANs) in electronic medical records (EMRs) is first-hand information to study the patient’s condition. Medical entity extraction of RANs is an important task to get disease information for medical decision-making. For Chinese electronic medical records, each medical entity contains not only word information but also rich character information. Effective combination of words and characters is very important for medical entity extraction. We propose a medical entity recognition model based on a character and word attention-enhanced (CWAE) neural network for Chinese RANs. In our model, word embeddings and character-based embeddings are obtained through character-enhanced word embedding (CWE) model and Convolutional Neural Network (CNN) model. Then attention mechanism combines the character-based embeddings and word embeddings together, which significantly improves the expression ability of words. The new word embeddings obtained by the attention mechanism are taken as the input to bidirectional long short-term memory (BI-LSTM) and conditional random field (CRF) to extract entities. We extracted nine types of key medical entities from Chinese RANs and evaluated our model. The proposed method was compared with two traditional machine learning methods CRF, support vector machine (SVM), and the related deep learning models. The result shows that our model has better performance, and the result of our model reaches 94.44% in the F1-score.

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Section: Related Workmentioning

confidence: 99%

“…• W-BILSTM: This model was used by Liubo Ouyang et al [13]. They used word embedding as input, and fed them into a bidirectional long short-term for named entity recognition.…”

Section: Comparison With Deep Learning Modelsmentioning

confidence: 99%

Medical Named Entity Extraction from Chinese Resident Admit Notes Using Character and Word Attention-Enhanced Neural Network

Gao

Wang

Wang³

et al. 2020

IJERPH

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“…Some RNN-based works have adopted a LSTM-CRF framework to strengthen the word sequential distributed representation for NER (e.g. [21][22][23][24][25][26][27]). For instance, Shijia et al [24] used Bi-LSTM to learn the hidden representations for characters based on the Character-Word Mixed Embedding (CWME).…”

Section: B Dl-based Neural Network For Ner Tasksmentioning

confidence: 99%

MoGCN: Mixture of Gated Convolutional Neural Network for Named Entity Recognition of Chinese Historical Texts

Yan

Su²,

Wang³

2020

IEEE Access

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Named Entity Recognition (NER) systems have been largely advanced by deep neural networks in the recent decade. However, the state-of-the-arts on NER have been less applied to Chinese historical texts due to the lack of standard corpora in Chinese historical domains and the difficulty of accessing a quality ancient corpus. This paper addresses the respective issues and proposes an efficient automatic processing solution for tackling NER of ancient Chinese data, including the implementation of data-driven tagging and an innovative end-to-end network namely "MoGCN" (Mixture of Gated Convolutional Neural Network). A corpus consisting of three genres of Chinese historical classics is generated by our tagging approach, which is experimented for uncovering the generalization ability of proposed model. The empirical analysis demonstrates that our proposed model achieves the best results with above 1.5% F1-score improvement over other sophisticated models in this dataset, where the experimental performance shows positive dependence on the quality of corpus. Furthermore, our model can perform much better on shorter entities especially for 2-charater ones, while many long-range entities can be only identified by our model based on our auxiliary attribute analysis. This work serves as a preliminary exploitation of NER for historical data, providing unique insights and reference values for similar tasks. Future work should be focused on more exploration about NER optimization on massive Chinese traditional texts with linguistic features and learning strategies. INDEX TERMS Named entity recognition, gated neural network, Chinese historical texts

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“…The authors in [14] proposed an improved NER system using deep learning module for Chinese text. Without using any manual feature engineering, the system can detect the word features automatically.…”

Section: Related Workmentioning

confidence: 99%

Bidirectional Recurrent Neural Network Approach for Arabic Named Entity Recognition

2018

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Recurrent neural network (RNN) has achieved remarkable success in sequence labeling tasks with memory requirement. RNN can remember previous information of a sequence and can thus be used to solve natural language processing (NLP) tasks. Named entity recognition (NER) is a common task of NLP and can be considered a classification problem. We propose a bidirectional long short-term memory (LSTM) model for this entity recognition task of the Arabic text. The LSTM network can process sequences and relate to each part of it, which makes it useful for the NER task. Moreover, we use pre-trained word embedding to train the inputs that are fed into the LSTM network. The proposed model is evaluated on a popular dataset called “ANERcorp.” Experimental results show that the model with word embedding achieves a high F-score measure of approximately 88.01%.

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Chinese Named Entity Recognition Based on B-LSTM Neural Network with Additional Features

Cited by 12 publications

References 15 publications

Medical Named Entity Extraction from Chinese Resident Admit Notes Using Character and Word Attention-Enhanced Neural Network

Medical Named Entity Extraction from Chinese Resident Admit Notes Using Character and Word Attention-Enhanced Neural Network

MoGCN: Mixture of Gated Convolutional Neural Network for Named Entity Recognition of Chinese Historical Texts

Bidirectional Recurrent Neural Network Approach for Arabic Named Entity Recognition

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