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Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging ...
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Entropy Analysis of Heart Rate Variability in Different Sleep Stages
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In: Entropy (Basel) (2022)
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Research Landscape of Artificial Intelligence and e-Learning: A Bibliometric Research
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In: Front Psychol (2022)
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The Effects of Digitally Mediated Multimodal Indirect Feedback on Narrations in L2 Spanish Writing: Eye Tracking as a Measure of Noticing
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In: Languages ; Volume 6 ; Issue 4 (2021)
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Application of Dual-Channel Convolutional Neural Network Algorithm in Semantic Feature Analysis of English Text Big Data
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In: Comput Intell Neurosci (2021)
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Abstract:
The current Internet data explosion is expecting an ever-higher demand for text emotion analysis that greatly facilitates public opinion analysis and trend prediction, among others. Therefore, this paper proposes to use a dual-channel convolutional neural network (DCNN) algorithm to analyze the semantic features of English text big data. Following the analysis of the effect of CNN, artificial neural network (ANN), and recurrent neural network (RNN) on English text data analysis, the more effective long short-term memory (LSTM) and the gated recurrent unit (GRU) neural network (NN) are introduced, and each network is combined with the dual-channel CNN, respectively, and comprehensively analyzed under comparative experiments. Second, the semantic features of English text big data are analyzed through the improved SO-pointwise mutual information (SO-PMI) algorithm. Finally, the ensemble dual-channel CNN model is established. Under the comparative experiment, GRU NN has a better feature detection effect than LSTM NN, but the performance increase from dual-channel CNN to GRU NN + dual-channel CNN is not obvious. Under the comparative analysis of GRU NN + dual-channel CNN model and LSTM NN + dual-channel CNN model, GRU NN + dual-channel CNN model ensures the high accuracy of semantic feature analysis and improves the analysis speed of the model. Further, after the attention mechanism is added to the GRU NN + dual-channel CNN model, the accuracy of semantic feature analysis of the model is improved by nearly 1.3%. Therefore, the ensemble model of GRU NN + dual-channel CNN + attention mechanism is more suitable for semantic feature analysis of English text big data. The results will help the e-commerce platform to analyze the evaluation language and semantic features for the current network English short texts.
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Keyword:
Research Article
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URL: http://www.ncbi.nlm.nih.gov/pubmed/34782834
https://doi.org/10.1155/2021/7085412
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8590597/
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Comparison of the Effects of Oral Midazolam and Intranasal Dexmedetomidine on Preoperative Sedation and Anesthesia Induction in Children Undergoing Surgeries
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In: Front Pharmacol (2021)
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Cross-cultural validation of the IRB Researcher Assessment Tool: Chinese Version
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In: BMC Med Ethics (2021)
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Mapping Natural Language Instructions to Mobile UI Action Sequences ...
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Individuals vs. BARD: Experimental Evaluation of an Online System for Structured, Collaborative Bayesian Reasoning
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In: Front Psychol (2020)
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Post-reform gender politics : how do Chinese internet users portray Theresa May on Zhihu
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Phonetic-enriched Text Representation for Chinese Sentiment Analysis with Reinforcement Learning ...
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| 13 |
Post Text Processing of Chinese Speech Recognition Based on Bidirectional LSTM Networks and CRF
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In: Electronics ; Volume 8 ; Issue 11 (2019)
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L2 Vocabulary Knowledge and L2 Listening Comprehension: A Structural Equation Model
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Back to the future?:How Chinese-English bilinguals switch between front and back orientation for time
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Norm talk and human cooperation: Can we talk ourselves into cooperation?
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L2 Vocabulary Knowledge and L2 Listening Comprehension: A Structural Equation Model
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In: Canadian Journal of Applied Linguistics; Vol. 22 No. 1 (2019): Special Issue: In Memory of Larry Vandergrift; 85-102 ; Revue canadienne de linguistique appliquée; Vol. 22 No. 1 (2019): Numéro spécial : à la mémoire de Larry Vandergrift; 85-102 ; 1920-1818 ; 1481-868X (2019)
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The role of segments and prosody in the identification of a speaker’s dialect
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Look deeper see richer: Depth-aware image paragraph captioning
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