Semantic Role Labeling Integrated with Multilevel Linguistic Cues and Bi-LSTM-CRF

Wan, Fucheng; Yang, Yimin; Zhu, Dachang; Yu, Hongzhi; Zhu, Ao; Che, Guoyi; Ma, Ning

doi:10.1155/2022/6300530

Cited by 9 publications

(10 citation statements)

References 9 publications

(11 reference statements)

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“…Before performing text sentiment analysis, data preprocessing on the original text is required. Common data preprocessing methods in NLP tasks include data cleaning, stop word removal, and Chinese word segmentation [14][15][16]. Tese three preprocessing methods are introduced below.…”

Section: Data Preprocessingmentioning

confidence: 99%

Network Public Opinion Risk Prediction and Judgment Based on Deep Learning: A Model of Text Sentiment Analysis

Yang

2022

Computational Intelligence and Neuroscience

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Under the background of the gradual development and popularization of mobile Internet information technology, this paper realizes network public opinion monitoring and emotion analysis based on the deep learning method, aiming at the research needs of people’s ideological changes and emotional trends. Aiming at the shortcomings of sentiment dictionaries or machine learning methods in sentiment analysis tasks, this paper builds a sentiment classification model based on deep learning methods. First, the current main text preprocessing methods are introduced, and then a sentiment classification model, BCBL, is proposed, combining BERT, CNN, and Bi LSTM. Compared with traditional models, BCBL can better complete text sentiment classification tasks on standard datasets. Next, in view of the problem that BCBL does not consider the distribution of vocabulary weights, an attention mechanism is introduced to improve BCBL, and then the BCBL-Att model is proposed. Set up multiple sets of comparative experiments again and find that the classification effect and overall performance of BCBL-Att on standard datasets are better than BCBL, indicating that BCBL-Att has more advantages in text sentiment classification tasks.

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Section: Data Preprocessingmentioning

confidence: 99%

Network Public Opinion Risk Prediction and Judgment Based on Deep Learning: A Model of Text Sentiment Analysis

Yang

2022

Computational Intelligence and Neuroscience

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“…In order to verify the effectiveness of the method in this paper, the mechanism of model fusion of CNN neural network, LSTM neural network and CRF is also discussed [26]. Eight groups of comparative experiments were set up, and the inputs were all weight ratio vectors.…”

Section: Experimental Analysis and Resultsmentioning

confidence: 99%

Research on the relationship between the construction of school teachers’ team literacy and the educational environment

Zou¹

2023

Applied Mathematics and Nonlinear Sciences

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Teachers are the leaders of teaching activities, and the quality of teachers is the core of their ability to effectively carry out teaching practice. The quality of teachers’ team is closely related to the educational environment. This paper first aims at the origin and development of the concept of literacy construction and believes that the literacy of teachers’ team in the educational environment is an extension of literacy, and it is also a necessary literacy for teaching. First, it is essential to carry out the calculation of the literacy matching weight. Second, the construction of teacher team literacy mainly includes teachers’ professional literacy, teacher teaching literacy, teacher information literacy, teacher learning literacy and teacher personal characteristics. The construction of team literacy is inseparable from the influence of environmental factors, mainly including the needs of school students, school innovation atmosphere, school education environment and other educational environmental factors. Combined with the quality of teachers’ team, a set of teacher team quality construction framework based on the educational environment is designed. At the same time, the use of information technology innovation and breakthroughs, combined with school teacher team literacy construction requires certain educational environment support, and it is necessary to provide targeted training for literacy construction, create a suitable teaching atmosphere, get rid of traditional teaching presuppositions, and start from the educational environment. The problems that appear in the process are used as a basis to improve the construction of literacy according to the problems. Finally, using deep learning to predict and analyse the data, the experimental results show that the accurate prediction can reach 96.89%, indicating that various factors in the educational environment will affect the construction of teacher team literacy.

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“…1. BiLSTM-CRF [30] model: a BiLSTM network is used to capture two-way semantic dependencies, and then capture global features; 2. BiGRU-CRF [31] model: replaces the BiLSTM network with a BiGRU network which uses fewer parameters; 3.…”

Section: Resultsmentioning

confidence: 99%

“…BiLSTM-CRF [30] model: a BiLSTM network is used to capture two-way semantic dependencies, and then capture global features; 2.…”

Section: Model Parametersmentioning

confidence: 99%

Construction of Power Fault Knowledge Graph Based on Deep Learning

et al. 2022

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A knowledge graph can structure heterogeneous knowledge in the field of power faults, construct the correlation between different pieces of knowledge, and solve the diversification, complexity, and island of fault data. There are many kinds of entities in power fault defect text, the relationship between entities is complex, and the data are often mixed with noise. It is necessary to research how to effectively mine the target data and separate the salient knowledge from the noise. Moreover, the traditional entity and relationship extraction methods used in the construction of a power fault knowledge graph cannot fully understand the text semantics, and the response accuracy is low. The Log system usually contains all kinds of information related to faults and a log analysis helps us collect fault information and perform association analysis. Therefore, a Bidirectional Sliced GRU with Gated Attention mechanism (BiSGRU-GA) model is proposed to detect the anomalous logs in the power system, this enriches the fault knowledge base and provides a good data resource for the construction of the knowledge graph. A new Bidirectional GRU with Gated Attention mechanism and Conditional Random Fields and a BERT input layer (BBiGRU-GA-CRF) model is proposed by introducing a BERT layer and Attention Mechanism into the Bidirectional GRU (BiGRU) model to more fully understand the context information of fault sentences and improve the accuracy of entity recognition of fault sentences. Aiming to solve the problems of large calculation cost and propagation error which occur in the traditional relationship extraction model, an improved Bidirectional Gated Recurrent Unit neural network with fewer parameters and the Gated Attention Mechanism (BiGRU-GA) model is proposed. This new model introduces an improved Gated Attention Mechanism to achieve better effects in relationship extraction. Compared with Bidirectional Long Short-Term Memory with Attention Mechanism (BiLSTM-Attention), the accuracy, recall, and F-measure of the model were improved by 1.79%, 13.83%, and 0.30% respectively, and the time cost is reduced by about 16%. The experimental results show that the BiGRU-GA model can capture local features, reduce the training time cost, and improve the model recognition effect.

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Semantic Role Labeling Integrated with Multilevel Linguistic Cues and Bi-LSTM-CRF

Cited by 9 publications

References 9 publications

Network Public Opinion Risk Prediction and Judgment Based on Deep Learning: A Model of Text Sentiment Analysis

Network Public Opinion Risk Prediction and Judgment Based on Deep Learning: A Model of Text Sentiment Analysis

Research on the relationship between the construction of school teachers’ team literacy and the educational environment

Construction of Power Fault Knowledge Graph Based on Deep Learning

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