Chinese named entity recognition in power domain based on Bi-LSTM-CRF

Zhao, Zhenqiang; Chen, Zhenyu; Wang, Zhibin; Huang, Yunhao; Gao, Xingyu; Di, Fangchun; Li, Lixin; Ji, Xiaohui

doi:10.1145/3357254.3357283

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Moreover, a decision function based on the CRF layer was used to generate the label sequence. CRF is a method to obtain global optimum predictions using a conditional probability distribution model [29]. The CRF layer labels the sequence using the surrounding labels.…”

Section: Classificationmentioning

confidence: 99%

Bidirectional long-short term memory and conditional random field for tourism named entity recognition

Zahra

Hidayatullah

Rani

2022

IJ-AI

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<p><span>The common thing to do when planning a trip is to search for a tourist destination. This process is often done using search engines and reading articles on the internet. However, it takes much time to search for such information, as to obtain relevant information, we have to read some available articles. Named entity recognition (NER) can detect named entities in a text to help users find the desired information. This study aims to create a NER model that will help to detect tourist attractions in an article. The articles used for the dataset are English articles obtained from the internet. We built our NER model using bidirectional long-short term memory (BiLSTM) and conditional random fields (CRF), with Word2Vec as a feature. Our proposed model achieved the best with an average F1-Score of 75.25% compared to all scenarios tested.</span></p>

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Section: Classificationmentioning

confidence: 99%

Bidirectional long-short term memory and conditional random field for tourism named entity recognition

Zahra

Hidayatullah

Rani

2022

IJ-AI

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“…Fan H et al [28] used semantic tagging information such as clauses, word segmentation, and part-of-speech tagging as a preprocessing method in NER for the entire business domain of the power grid. Zhao Z Q et al [29] used a BiLSTM-CRF model on two categories of power data. Jiang C et al [30] proposed a NER model combining BERT, Bi-LSTM, and CRF for power domain.…”

Section: Related Workmentioning

confidence: 99%

Named Entity Recognition in Electric Power Metering Domain Based on Attention Mechanism

Zheng

Sun

Wang³

et al. 2021

IEEE Access

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Named Entity Recognition(NER) is one key step for constructing power domain knowledge graph which is increasingly urgent in building smart grid. This paper proposes a new NER model called Att-CNN-BiGRU-CRF which consists of the following five layers. A joint feature embedding layer combines the character embedding and word embedding based on BERT to obtain more semantic information. A convolutional attention layer combines the local attention mechanism and CNN to capture the local context relationship. A BiGRU layer extracts higher-level features of power metering text. A global multi-head attention layer optimizes the processing of sentence level information. A CRF layer obtains the output tag sequences. This paper also constructs a corresponding power metering corpus data set with a new entity classification method. Experimental results show that the proposed model has high recall rate 88.16% and precision rate 89.33% which is better than the state-of-the-art models.INDEX TERMS power metering, attention mechanism, joint feature, named entity recognition.

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“…After obtaining the contextual feature representation, Label decoding layer predicts the entity label to generate the corresponding output label sequence. Conditional Random Fields [53][54][55] are currently recognized label decoders used in deep learning for NER tasks. The main reason is that CRF considers the interdependence between tags on the basis of text information modeling, so as to get a better solution.…”

Section: Model Frameworkmentioning

confidence: 99%

A review of Chinese named entity recognition

Cheng

Liu

et al. 2021

KSII TIIS

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Named Entity Recognition (NER) is used to identify entity nouns in the corpus such as Location, Person and Organization, etc. NER is also an important basic of research in various natural language fields. The processing of Chinese NER has some unique difficulties, for example, there is no obvious segmentation boundary between each Chinese character in a Chinese sentence. The Chinese NER task is often combined with Chinese word segmentation, and so on. In response to these problems, we summarize the recognition methods of Chinese NER. In this review, we first introduce the sequence labeling system and evaluation metrics of NER. Then, we divide Chinese NER methods into rule-based methods, statistics-based machine learning methods and deep learning-based methods. Subsequently, we analyze in detail the model framework based on deep learning and the typical Chinese NER methods. Finally, we put forward the current challenges and future research directions of Chinese NER technology.

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Chinese named entity recognition in power domain based on Bi-LSTM-CRF

Cited by 5 publications

References 15 publications

Bidirectional long-short term memory and conditional random field for tourism named entity recognition

Bidirectional long-short term memory and conditional random field for tourism named entity recognition

Named Entity Recognition in Electric Power Metering Domain Based on Attention Mechanism

A review of Chinese named entity recognition

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