Research and analysis of intelligent English learning system based on improved neural network

Yin, Meng

doi:10.3233/jifs-179946

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…The entity grid is extended into a graph structure, and an entity-based unsupervised graph model is proposed, which first represents the text as a bipartite graph structure of sentences and entities, and then this bipartite graph is projected using three different ways, and when the same entity exists between sentences, an edge is created between two sentence nodes to represent the whole text as an entity graph, and the average of the entity graph is calculated. The coherence of the text is analyzed by calculating the average degree of the entity graph [ 6 ]. The entity graph model optimized embedding world knowledge based on the entity graph to capture those entities that are semantically related.…”

Section: Related Workmentioning

confidence: 99%

Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model

Hsiao

Hung

2022

Computational Intelligence and Neuroscience

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This paper presents an in-depth study and analysis of the model of English writing using artificial intelligence algorithms of neural networks. Based on word vectors, the unsupervised disambiguation, and clustering of multimedia contexts extracted from massive online videos, the disambiguation accuracy reaches over 0.7, and the resulting small-scale multimedia context set can cover up to 90% of vocabulary learning tasks; user experiments show that the multimedia context learning system based on this method can improve the effectiveness and experience of ESL vocabulary learning, as well as the long-term word sense memory of learners. The results are 30% better. Based on the dependency grammatical relations and semantic metrics of collocations on a large-scale professional corpus, we established a collocation intention description and retrieval method in line with users’ linguistic cognition and doubled the usage rate of collocation retrieval on the actual deployment system after half a year, becoming a user “sticky” ESL writing aid, and further defined style. Dictionaries only provide basic lexical definitions, and, even if supported by example sentences, they still cannot meet the needs of ESL authors in terms of expressive accuracy and richness. However, the current machine translation is based on the black box deep neural network construction, and its translation process is not understandable and interactive. Among the three algorithmic models constructed in this paper, the multitask learning model outperforms the conditional random field model and the LSTM-CRF model because the multitask learning model with auxiliary tasks solves the problem of sparse data to a certain extent, allowing the model to be trained more adequately in the case of uneven label distribution, and thus performs better than other models in the task of grammatical error detection.

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Section: Related Workmentioning

confidence: 99%

Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model

Hsiao

Hung

2022

Computational Intelligence and Neuroscience

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“…Among them, the similarity algorithm is the key technology in the intelligent Q&A system, which is used to realize the finding of the most similar question in the knowledge ontology database. By calculating the similarity between the questions asked by the user and the answers stored in the knowledge ontology database, the answer with the highest similarity is selected as the answer to the question asked by the user, and the corresponding answer information is returned to the user [16].…”

Section: Related Workmentioning

confidence: 99%

Design and Implementation of English Intelligent Communication Platform Based on Similarity Algorithm

Yu-jie

2021

Complexity

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Intelligent communication processing in English aims to obtain effective information from unstructured text data using various text processing techniques. Text vector representation and text similarity calculation are important fundamental tasks in the whole field of natural language processing. In response to the shortcomings of existing sentence vector representation models and the singularity of text similarity algorithms, improved models and algorithms are proposed based on a thorough study of related domain technologies. This paper presents an in-depth and comprehensive study of text vectorization representation and text similarity calculation algorithms in the field of natural language processing. The existing text vectorized representation models and text similarity computation algorithms are described, and their shortcomings are summarized to provide a basis for the background and significance of this paper, as well as to provide ideas for improvement directions. It is experimentally verified that the sentence vector model proposed in this paper achieves higher accuracy than the SIF sentence vector model for text classification tasks. In the task of text similarity computation, it achieves better results in three evaluation metrics: accuracy, recall, and F1 value. The algorithm also improves the computational efficiency of the model to a certain extent by removing feature words with low feature contribution. The algorithm first improves the deficiencies of the traditional word-shift distance algorithm by defining multifeature fusion weights and realizes a text similarity calculation algorithm based on multifeature weighted fusion with better similarity calculation results. Then, a linear weighting model is constructed to further combine the similarity calculation results of the hierarchical pooled IIG-SIF sentence vectors to realize the multimodel fusion text similarity calculation algorithm.

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“…In order to gather the steam system data together, it is necessary to establish a real-time database for the steam system, collect and save all the steam data, and connect with the relational database of the ABB Industrial IT control system to realize the integration of basic data and optimized data. Since this system involves the entire plant area and has a wide range of points, how to integrate all the steam system data into a real-time database is a very difficult problem [6]. When considering the introduction of data, the following principles are mainly followed: -It is technically feasible.…”

Section: Data Collection Processmentioning

confidence: 99%

Error analysis system of industrial steam heat flow based on neural network computer simulation

Xiao

Chen

2021

THERM SCI

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The paper focuses on the analysis of the neural network mathematical model for temperature and pressure compensation in steam flow measurement, summarizes two types of compensation models suitable for DCS configuration, and introduces the realization of steam flow measurement in ABB Industrial TDCS configuration ways and methods of temperature and pressure compensation. At the same time, the paper uses the scientific neural network intelligent model control method to analyze, optimize, configure and manage the information, improve the operation and management level of the steam system, and realize the optimized operation of the steam system, so as to achieve the purpose of energy saving and consumption reduction.

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Research and analysis of intelligent English learning system based on improved neural network

Cited by 9 publications

References 5 publications

Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model

Construction of an Artificial Intelligence Writing Model for English Based on Fusion Neural Network Model

Design and Implementation of English Intelligent Communication Platform Based on Similarity Algorithm

Error analysis system of industrial steam heat flow based on neural network computer simulation

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