An intelligent work order classification model for government service based on multi-label neural network

Huang, Weidong; Su, Chong; Wang, Yuan

doi:10.1016/j.comcom.2021.02.020

Cited by 6 publications

(5 citation statements)

References 18 publications

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“…In this paper, TextRCNN is used to extract the depth features of the microblog text. Transformer, TextRNN_Att, TextCNN, DPCNN, and TextRNN are used as comparison models, which are extensively utilized in the field of natural language processing (Huang et al, 2021;Yang et al, 2022. Anaconda + Python3.7 + Pytorch1.13.1 is The accuracy, recall, and F values of all classifiers are presented in Table 7.…”

Section: Results Of Textrnn Extracting Deep Featuresmentioning

confidence: 99%

“…The model combines the advantages of convolutional neural networks and time series neural networks. As shown in Figure 4, first, word embedding is performed on the microblog text; second, the word vector is encoded from both positive and negative directions with a bidirectional long‐short‐term memory network model (BiLSTM) to mine long‐distance dependencies and contextual semantic knowledge (Huang et al, 2021; Yang et al, 2022).…”

Section: Research Frameworkmentioning

confidence: 99%

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Text‐based soybean futures price forecasting: A two‐stage deep learning approach

Wang

Zeng

2022

Journal of Forecasting

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This paper investigates the soybean futures price prediction problem from a new perspective and proposes an effective prediction model named Two‐Stage Hybrid Long Short‐Term Memory (TSH‐LSTM) by using text data from social media. First, the unstructured text is transformed into structured data by sentiment analysis and text classification methods. The improved sentiment score is computed by combining the degree centrality of sentiment words based on the sentiment dictionary method, and the characteristics of price fluctuations in texts are learned through the text Recurrent Convolutional Neural Networks. Second, the significant relationship between social media features and soybean futures price is assessed through stepwise regression, and the results of such an assessment are used as a basis for the identification of significant factors as input variables of the prediction model. Finally, the TSH‐LSTM prediction model is designed, and the final prediction result is acquired through the combination of prediction results of each stage using the error reciprocal method. The empirical results indicate that the incorporation of the social media text feature helps improve forecasting performances. Specifically, the proposed TSH‐LSTM is more accurate than univariate LSTM, multivariate LSTM, and eXtreme Gradient Boosting.

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Section: Results Of Textrnn Extracting Deep Featuresmentioning

confidence: 99%

Section: Research Frameworkmentioning

confidence: 99%

Text‐based soybean futures price forecasting: A two‐stage deep learning approach

Wang

Zeng

2022

Journal of Forecasting

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Section: Resultsmentioning

confidence: 99%

“…For the MPOA data set, the text category is 2, the average sentence length is 3, the training set contains 9,500 words, and the testing set contains 1,105 words. The classification effects of LSTM (Chen et al, 2020), Bi-LSTM (Ye et al, 2019), Text CNN (Xie et al, 2020), Text RCNN (Huang et al, 2021), attention-based LSTM (ALSTM) (Liu et al, 2018), and attention-based Bi-LSTM (ABi-LSTM) (Song et al, 2018) The difference in classification accuracy of different models on different data sets is quantitatively analyzed, and the results are shown in Table 1. Compared with the LSTM, Bi-LSTM, TextCNN, TextRCNN, ALSTM, and ABi-LSTM models, the classification accuracy of the ALGCNN model constructed on the MR data set is improved by 26.5, 32.3, 6.6, 3.9, 9.0, and 3.5%, respectively; the classification accuracy on the SST data set of the ALGCNN model constructed has increased by 25.0, 29.8, 2.0, 0.0, 14.2, and 8.0%, respectively; and the classification accuracy rate on the MPQA data set has increased by 20.6, 17.3, 6.0, 2.1, 9.2, and 2.9%, respectively.…”

Section: Simulation Verification Of Algcnn Modelmentioning

confidence: 99%

The Influencing Legal and Factors of Migrant Children’s Educational Integration Based on Convolutional Neural Network

Zhang

Wang

Zhou³

et al. 2022

Front. Psychol.

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This research aims to analyze the influencing factors of migrant children’s education integration based on the convolutional neural network (CNN) algorithm. The attention mechanism, LSTM, and GRU are introduced based on the CNN algorithm, to establish an ALGCNN model for text classification. Film and television review data set (MR), Stanford sentiment data set (SST), and news opinion data set (MPQA) are used to analyze the classification accuracy, loss value, Hamming loss (HL), precision (Pre), recall (Re), and micro-F1 (F1) of the ALGCNN model. Then, on the big data platform, data in the Comprehensive Management System of Floating Population and Rental Housing, Student Status Information Management System, and Student Information Management System of Beijing city are taken as samples. The ALGCNN model is used to classify and compare related data. It is found that in the MR, STT, and MPQA data sets, the classification accuracy and loss value of the ALGCNN model are better than other algorithms. HL is the lowest (15.2 ± 1.38%), the Pre is second only to the BERT algorithm, and the Re and F1 are both higher than other algorithms. From 2015 to 2019, the number of migrant children in different grades of elementary school shows a gradual increase. Among migrant children, the number of migrant children from other counties in this province is evidently higher than the number of migrant children from other provinces. Among children of migrant workers, the number of immigrants from other counties in this province is also notably higher than the number of immigrants from other provinces. With the gradual increase in the years, the proportion of township-level expenses shows a gradual decrease, whereas the proportion of district and county-level expenses shows a gradual increase. Moreover, the accuracy of the ALGCNN model in migrant children and local children data classification is 98.6 and 98.9%, respectively. The proportion of migrant children in the first and second grades of a primary school in Beijing city is obviously higher than that of local children (p < 0.05). The average final score of local children was greatly higher than that of migrant children (p < 0.05), whereas the scores of migrant children’s listening methods, learning skills, and learning environment adaptability are lower, which shows that an effective text classification model (ALGCNN) is established based on the CNN algorithm. In short, the children’s education costs, listening methods, learning skills, and learning environment adaptability are the main factors affecting migrant children’s educational integration, and this work provides a reference for the analysis of migrant children’s educational integration.

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“…Textual implication recognition, also known as natural language inference, is a fundamental yet challenging task in the field of natural language processing [4]. The goal of this task is to determine the directed semantic relationship between two consecutive texts, where the embodied antecedent is noted as the text T and the embodied consequent is noted as hypothesis H [5].…”

mentioning

confidence: 99%

A Multilabel Learning-Based Automatic Annotation Method for Semantic Roles in English Text

Lei,

Wang

2023

IEEE Access

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With the increasing amount of textual information in the Internet, smart semantic comprehension is a practical demand. Among, automatic annotation for semantic roles remains the fundamental part for effective semantic comprehension. Although machine learning-based methods had received much attention in recent years, they mostly divided each sentences into separable parts for calculation. To deal with such challenge, this paper introduces multilabel learning to propose a novel automatic annotation method for semantic roles in English text. In the semantic representation of words, the method uses convolutional neural networks to extract local feature information of words from the character level. Such design can alleviate the problem of inconspicuous semantic features caused by random initialization of unregistered words. Secondly, in the process of implication recognition, by combining the interactive attention mechanism to construct a capsule for each implication relation separately, the recognition of the final implication relation is completed in the way of categorical learning. At last, some experiments are conducted on real-world data to verify the proposed method with being compared with several typical relevant methods. The obtained results show that the proposal achieves better Macro-F1 results on eight datasets compared to seven algorithms. Besides, the proposal also performs better than others in the sensitivity testing, as its performance can remain stable with the increase of noise input. In summary, the proposal can achieve good results and show strong capability in semantic role labeling tasks.

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An intelligent work order classification model for government service based on multi-label neural network

Cited by 6 publications

References 18 publications

Text‐based soybean futures price forecasting: A two‐stage deep learning approach

Text‐based soybean futures price forecasting: A two‐stage deep learning approach

The Influencing Legal and Factors of Migrant Children’s Educational Integration Based on Convolutional Neural Network

A Multilabel Learning-Based Automatic Annotation Method for Semantic Roles in English Text

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