Power entity recognition based on bidirectional long short-term memory and conditional random fields

Ji, Zhixiang; Wang, Xiaohui; Cai, Changyu; Sun, Hongjian

doi:10.1016/j.gloei.2020.05.010

Cited by 15 publications

(11 citation statements)

References 2 publications

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“…The CRF model achieved an accuracy of 83%, indicating that the it can identify power entities better than the BLSTM model. In addition, P. Chen et al [23] counted word frequencies in power system text through the TF-IDF algorithm. By ranking the TF results, they were able to extract critical TF height information.…”

Section: Related Workmentioning

confidence: 99%

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Entity Extraction of Electrical Equipment Malfunction Text by a Hybrid Natural Language Processing Algorithm

Kong

Yue²,

Shi

et al. 2021

IEEE Access

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Many electrical equipment malfunction text messages are collected during power system operation and maintenance procedures. These texts usually contain crucial information for maintenance and condition monitoring. Because these power system malfunction texts are characterized by multidomain vocabularies, complex-syntactic structures, and long sentences, it is challenging to for automated systems to capture their semantic meaning and essential information. To address this issue, we propose a hybrid natural language processing (hybrid-NLP) algorithm to extract entities that represent electrical equipment. This algorithm is composed of a dictionary-based method, a language technology platform (LTP) tool, and the bidirectional encoder representations from a transformers-conditional random field (BERT-CRF) model. Significantly, the softmax output layer of the bidirectional encoder representations from the transformers (BERT) model is replaced by the conditional random field (CRF) algorithm to strengthen the contextual relationships between words and thus solve the local optimization of the preferred word label. The effectiveness of the proposed hybrid-NLP method is verified on a realistic dataset. Moreover, a statistical analysis is conducted to provide a reference for the operation and maintenance of power systems. INDEX TERMS electrical equipment malfunction text, natural language processing, entity extraction, BERT-CRF model.

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Section: Related Workmentioning

confidence: 99%

“…It is difficult to address this issue using a single NER method. At present, no acceptable NER framework exists for performing comprehensive entity extraction from electrical equipment malfunction texts of due to the abovementioned complexities [13].…”

Section: Introductionmentioning

confidence: 99%

Entity Extraction of Electrical Equipment Malfunction Text by a Hybrid Natural Language Processing Algorithm

Kong

Yue²,

Shi

et al. 2021

IEEE Access

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“…Finally, the BiLSTM model receives the feature vectors as input and carried out the recognition process. The LSTM refers to a special RNN model that resolves the problems of gradient vanishing of the RNN by presenting a threshold mechanism and memory unit [20]. But 𝑥 represent the network input at distinct times, 𝑦 indicates the network output, ℎ represents the hidden layer, 𝑢 signifies the weight from input to the hidden layers, 𝑤 denote the weights of prior node hidden layer to the present node hidden layer, and 𝑣 represents the weight from hidden to the output layers.…”

Section: Design Of Ao Based Bilstm Model For Recognition Processmentioning

confidence: 99%

Automated Telugu Printed and Handwritten Character Recognition in Single Image using Aquila Optimizer based Deep Learning Model

Sonthi¹,

Nagarajan²,

Krishnaraj³

2021

IJACSA

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Machine printed or handwritten character recognition becomes a major research topic in several real time applications. The recent advancements of deep learning and image processing techniques can be employed for printed and handwritten character recognition. Telugu character Recognition (TCR) remains a difficult task in optical character recognition (OCR), which transforms the printed and handwritten characters into respective text formats. In this aspect, this study introduces an effective deep learning based TCR model for printed and handwritten characters (DLTCR-PHWC). The proposed DLTCR-PHWC technique aims to detect and recognize the printed as well as handwritten characters that exist in the same image. Primarily, image pre-processing is performed using the adaptive fuzzy filtering technique. Next, line and character segmentation processes are performed to derive useful regions. In addition, the fusion of EfficientNet and CapsuleNet models is used for feature extraction. Finally, the Aquila optimizer (AO) with bi-directional long short-term memory (BiLSTM) model is utilized for recognition process. A detailed experimentation of the proposed DLTCR-PHWC technique is investigated using Telugu character dataset and the simulation outcome portrayed the supremacy of the proposed DLTCR-PHWC technique over the recent state of art approaches.

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“…Power metering entity recognition is a kind of Named Entity Recognition (NER), and the early machine learning methods for NER can be classified as statistical machine learning models and methods based on dictionaries or rules. In recent years, deep learning models are widely used in power metering and applications [4,5]. Some commonly used deep neural networks have been applied for power metering entity recognition, such as Long Short-Term Memory (LST-M) [4], Convolutional Neural Network [6] and Transformer [7].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, deep learning models are widely used in power metering and applications [4,5]. Some commonly used deep neural networks have been applied for power metering entity recognition, such as Long Short-Term Memory (LST-M) [4], Convolutional Neural Network [6] and Transformer [7]. However, these existing models do not fully consider the situation that some power metering entities' names are partially overlapping and some power entities' names are very similar, especially in Chinese corpus, the word segmentation process will affect Chinese entity recognition.…”

Section: Introductionmentioning

confidence: 99%

Constructing Bi-Order-Transformer-CRF With Neural Cosine Similarity Function for Power Metering Entity Recognition

et al. 2021

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In recent years, knowledge graphs are applied to provide knowledge support and data support for power grid monitoring and decision-making. To construct a power metering knowledge graph, the power metering entities should be effectively recognized and extracted. However, the existing machine learning models do not fully consider the situation that some power metering entities' names are partially overlapping and boundaries of some power metering entities are fuzzy. In this paper, we propose a Biorder-Transformer-CRF to recognize power metering entities. Specifically, to alleviate the problem of fuzzy entity boundaries, we train our power metering word-vectors, and then we design Neural Cosine Similarity Function for distinguishing similar entities and Bi-order Feature Extracting Mechanism for recognizing overlapping entity names in the proposed Bi-order-Transformer-CRF. Moreover, we analyze the complexity of the proposed methods and verify that Bi-order-Transformer-CRF achieves better power metering entity recognition results compared with the commonly used machine learning methods in experiments.INDEX TERMS power metering; entity recognition; Neural Network; pattern recognition;

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Power entity recognition based on bidirectional long short-term memory and conditional random fields

Cited by 15 publications

References 2 publications

Entity Extraction of Electrical Equipment Malfunction Text by a Hybrid Natural Language Processing Algorithm

Entity Extraction of Electrical Equipment Malfunction Text by a Hybrid Natural Language Processing Algorithm

Automated Telugu Printed and Handwritten Character Recognition in Single Image using Aquila Optimizer based Deep Learning Model

Constructing Bi-Order-Transformer-CRF With Neural Cosine Similarity Function for Power Metering Entity Recognition

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