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Traditional English classroom teaching is difficult to meet the oral learning needs of most learners. Thanks to the continuous advancement of speech processing technology, computer-assisted language learning systems are becoming more intelligent, not only pointing out learners' pronunciation errors but also assessing their overall pronunciation level. Method. This paper uses the method of tree kernel function to measure the similarity of two ontology trees. According to the features of nodes in ontology tree, methods to calculate the external features and internal features of nodes are proposed, respectively. External features are mainly obtained by calculating the hierarchical centrality, node density, and node coverage of nodes in the ontology tree; internal features are mainly obtained by measuring the richness of internal information. According to the similarity of ontology tree and the external features and internal features of nodes, the calculation formula of structural comprehensive similarity is improved, and the features of ontology itself can be fully considered in the calculation. According to the difference of the structure, the weights of the corresponding features during the calculation are adjusted autonomously, so that the calculation results are closer to reality. In spectral image preprocessing, endpoint detection utilizes the harmonic characteristics presented by narrowband spectrograms with high frequency resolution and eliminates useless nonspeech segments by detecting the presence of voiced segments. When building the neural network model, four convolutional layers, two fully connected layers, and one softmax output layer were conceived, and dropout was used to randomly suspend the work of some neurons to avoid overfitting. Results/Discussion. Through the data analysis of mean and variance and verified by one-way analysis of variance, it proves that the sentiment evaluation method in this paper is effective. The traditional multiple linear regression method is not suitable for the corpus and application scenarios of this paper. This paper proposes a decision tree structure, which is similar to the overall scoring process of raters, and uses the Interactive Dicremiser version 3 (ID3) algorithm to build a comprehensive evaluation decision tree for pitch, rhythm, intonation, speech rate, and emotion indicators. It is proved by experiments that the accurate consistency rate of the human-machine evaluation in this paper is 93%, the adjacent consistency rate is 96%, and the Pearson correlation coefficient value of the human-machine evaluation results is 0.89. The data results prove that the evaluation method in this paper is credible. [ABSTRACT FROM AUTHOR]

In the current era of information technology, people's requirements for English translation are gradually increasing, and the need for a computer to understand and translate English language is becoming more urgent. In order to accurately identify phrases, this paper proposes an English translation recognition model based on optimized GLR algorithm, which can improve the accuracy of recognition by locating phrases in the text. [ABSTRACT FROM AUTHOR]

In the era of deep learning, representational text-matching algorithms based on BERT and its variant models have become mainstream and are limited by the sentence vectors generated by the BERT model, and the SimCSE algorithm proposed in 2021 has improved the sentence vector quality to a certain extent. In this paper, to address the problem that the SimCSE algorithm has—that the greater the difference in sentence length, the smaller the probability that the sentence pairs are similar—an EdaCSE algorithm is proposed to perturb the sentence length using a simple data enhancement method without affecting the semantics of the sentences. The perturbation is applied to the sentence length by adding meaningless English punctuation marks to the original sentence so that the model no longer tends to recognise sentences of similar length as similar sentences. Based on the BERT series of models, experiments were conducted on five different datasets, and the experiments proved that the EdaCSE method improves an average of 1.67, 0.84, and 1.08 on the five datasets. [ABSTRACT FROM AUTHOR]

In order to solve the intelligent evaluation of English writing, this paper proposes a method based on the English semantic neural network algorithm. This paper first briefly analyzes the research background of the English semantic analysis system, then expounds on the relevant technologies of the English distance similarity algorithm, semantic analysis intelligent algorithm structure, word analysis algorithm, sentence part of speech analysis algorithm, sentence semantic analysis algorithm, and neural network algorithm, and finally expounds the database and method implementation of the English semantic analysis system, so as to provide guarantee for the design of the English semantic analysis system. The experimental results show that the recognition accuracy of the BRF network for English characters can reach 96.35%, which is 7.79% higher than that of the BP network; the AUC of the BRF network reaches 0.89, which is closer to 1 compared with 0.72 of the BP network. The test results are in good agreement with the antinoise curve test results of the figure. It is proved that the English semantic neural network algorithm can effectively improve the accuracy of English translation and further improve the efficiency of the system. [ABSTRACT FROM AUTHOR]

English translation, also a kind of language conversion, refers to the activities of expressing each other in English to express another language or another language to express English. This paper aimed to evaluate and research methods of English translation based on the improved Gaussian mixture model (GMM). The paper proposed a simple evaluation and analysis of English translation using a discriminative training algorithm. In the experimental part, on the one hand, taking students, teachers, and translators as case study objects, through a questionnaire survey, it can be known that the number of students who scored more than 80 points in the English translation test is increasing every year, from 11.30% in the first year to 21.13% in the fourth year, an increase of nearly 10%. On the other hand, it can be seen from the speech translation experiment that the correct translation rate was 46.18% by using intelligent technology to distinguish translation. The experimental results showed that the discriminative training algorithm under the improved GMM algorithm is effective in the research of English translation evaluation. [ABSTRACT FROM AUTHOR]

In order to accurately extract the useful information in English, this paper studies English text analysis combined with a genetic algorithm and establishes a text analysis system. In this method, a text tendency analysis algorithm based on a genetic algorithm language model is proposed, and a Doc2vec text feature representation algorithm integrating the LDA model is designed; the parallelization technology of text analysis algorithm is studied, and the parallelization model of the algorithm by using spark big data platform is designed; the process of English text tendency analysis is studied, and a Chinese text analysis system is designed and implemented based on big data platform, including corpus intake, corpus annotation, corpus storage, model training, model verification, and other modules. In order to verify the feasibility of this subject, the accuracy of the Doc2vec text feature representation algorithm of the fused LDA model designed in the prototype system is tested. The experimental results show that the fused text representation model has high recognition degree, and the AUC value of the ROC curve reaches 0.95. At the same time, this paper tests the text analysis-related algorithms involved in the system. The test results show that the parallel algorithm can greatly improve the efficiency of the system. [ABSTRACT FROM AUTHOR]

Traditional English teaching focuses on exam-oriented education. Although many students have passed various English tests and even achieved high scores, they cannot communicate freely in English. In order to change this situation, the state has adjusted the syllabus to enhance students' audiovisual (AV) and oral ability. With more attention paid to the course of audiovisual speaking, the improvement of teaching quality has become an important part. The rationality of teaching effect evaluation plays a key role. Therefore, it is necessary to establish a scientific and reasonable AV and oral teaching effect evaluation model. Based on this, this paper proposes an evaluation algorithm of English AV and oral teaching effect based on deep learning (DL). Its characteristics are as follows: firstly, a data-based objective evaluation method of teaching effect is constructed, and then the adaptive algorithm is used to optimize the initial weight and threshold of deep neural network. English teaching implementation shows to us that English language characteristic and the matter of actual activities are closely connected to the context. English learning should not only pay attention to the understanding of English vocabulary and sentence and also carry out cultural exchanges with the countries, but English is the common language. The experimental results show that this model improves the prediction accuracy and convergence speed of teaching quality evaluation results, providing a more feasible scheme for the evaluation of English AV teaching effect. [ABSTRACT FROM AUTHOR]

The demand of C language informatization for English learning has increased greatly, and strengthening the application of informatization in teaching has become a current trend, while deep learning algorithms have been applied in various tasks due to their obvious advantages. In this paper, an English score prediction method based on the XGBoost algorithm is proposed. In order to verify the effectiveness of the model in English score prediction, the principle of the XGBoost algorithm is firstly analyzed as a basis. The English test scores of a university for 2019–2021 were used as the basic data source, and the output probabilities of the proposed model were used to compare the results under different conditions. The experimental results show that the predicted scores are basically consistent with the actual scores. From a scientific point of view, the ability to predict unknown data with low error suggests that it enables students and teachers to identify the underlying factors that make it difficult for students to answer questions. Understanding these causes is useful for designing high-quality courses and lesson plans. [ABSTRACT FROM AUTHOR]

Collaborative filtering is the most widely used and successful recommendation technology in recommendation systems. In view of the problem that the effect of collaborative filtering technology depends on an accurate similarity measurement method and its poor scalability can not deal with big data, in the information age of computer development, all undertakings are developing rapidly. English has long been an important tool for people to communicate with each other, but due to the limitation of learning mode and environment, the level of Chinese oral English is generally low. The cloud computation-based oral English learning system is designed. Users can learn oral English knowledge, simulate oral English testing and establish exclusive dictionaries through mobile terminals. According to the specific learning situation of Chinese English learners, a flexible and expandable virtual interactive platform is constructed through virtual technology and artificial intelligence, and multi-dimensional virtual scenes and intelligent AI roles are designed to realize the communication and learning of AI roles in different scenarios and improve learners' oral ability and language sense knowledge. However, compared with the traditional self-help learning method, there are huge changes. Our modern English self-help learning has basically got rid of the limitation of paper books, and most of it is carried out through the computer network. [ABSTRACT FROM AUTHOR]

This work provides an enhanced attention model by addressing the drawbacks of standard English semantic analysis methods. This work provides the semantic component analysis and intelligent algorithm structure in order to investigate the intelligent algorithm of sentence component-focused English semantic analysis. In addition, the whole process of intelligently analyzing English semantics is investigated. In the process of English semantic analysis, semantic ambiguity, poor semantic analysis accuracy, and incorrect quantifiers are continually optimized and solved based on semantic analysis. It improves the accuracy of English semantic analysis. In the long sentence semantic analysis test, improving the performance of attention mechanism semantic analysis model is also ideal. It is proved that the performance of the proposed algorithm model is obviously improved compared with the traditional model in order to continuously promote the accuracy and quality of English language semantic analysis. [ABSTRACT FROM AUTHOR]

Although natural language processing technology has shown strong performance in many tasks, it is very vulnerable to adversarial examples, i.e., sentences with some small perturbations can fool AI models. Current adversarial texts for English are usually generated by finding substitute words in adjacent spaces of keyword vectors. Unlike English, Chinese is more discrete and has a more complex font structure, which words that are closer in vector spaces may differ greatly in physical structure. Therefore, adversarial examples generated by current methods possess lower quality and can be easily perceived by human, or rather, they are not suitable for the human cognitive system. In this paper, we propose the "WordIllusion", a new detectable black-box algorithm used for generating Chinese adversarial texts. In this method, we create a CKSF evaluation indicator to select the key words of sentences. And then, based on the shape bias of human cognitive system and the rectification understanding to create replacement spaces of key words. To verify the effectiveness of WordIllusion, we experiment with two types of text classification tasks by using six natural language processing models. The result indicates that our method is able to improve the accuracy rate efficiently, and the generated adversarial texts can be very misleading. [ABSTRACT FROM AUTHOR]

With the development of modern intelligent recognition, many intelligent recognition translation tools have emerged. These translation tools mainly include machine learning, neural network, KNN, and other artificial intelligence technologies. These technologies have been applied to many fields. Among them, machine translation is the most important and widely used one. A large number of English translation technologies have appeared in this development era. However, the translation accuracy of intelligent recognition technology cannot be guaranteed. Under the background of this English translation environment, we design an intelligent recognition algorithm of English translation based on the BP neural algorithm to improve the rationality of English translation and analyze the intelligent recognition model of English translation. The following conclusions are drawn from the experimental comparison Four Methods of Translation. (1) Compared with some similar algorithms, machine translation based on the BP neural algorithm has many characteristics, such as convenience, which are very suitable for English translation. (2) The intelligent recognition model of English translation using the BP neural network makes the sentence flow higher, which can solve some problems in translation and achieve coherent translation in context. (3) The intelligent recognition model with the BP neural network as the core realizes a variety of permutations and combinations of different characteristics of complex English sentences, solves many poor English sentences, and significantly improves the accuracy of English translation. [ABSTRACT FROM AUTHOR]

Background: Pain description is fundamental to health care. The McGill Pain Questionnaire (MPQ) has been validated as a tool for the multidimensional measurement of pain; however, its use relies heavily on language proficiency. Although the MPQ has remained unchanged since its inception, the English language has evolved significantly since then. The advent of the internet and social media has allowed for the generation of a staggering amount of publicly available data, allowing linguistic analysis at a scale never seen before. Objective: The aim of this study is to use social media data to examine the relevance of pain descriptors from the existing MPQ, identify novel contemporary English descriptors for pain among users of social media, and suggest a modification for a new MPQ for future validation and testing. Methods: All posts from social media platforms from January 1, 2019, to December 31, 2019, were extracted. Artificial intelligence and emotion analytics algorithms (Crystalace and CrystalFeel) were used to measure the emotional properties of the text, including sarcasm, anger, fear, sadness, joy, and valence. Word2Vec was used to identify new pain descriptors associated with the original descriptors from the MPQ. Analysis of count and pain intensity formed the basis for proposing new pain descriptors and determining the order of pain descriptors within each subclass. Results: A total of 118 new associated words were found via Word2Vec. Of these 118 words, 49 (41.5%) words had a count of at least 110, which corresponded to the count of the bottom 10% (8/78) of the original MPQ pain descriptors. The count and intensity of pain descriptors were used to formulate the inclusion criteria for a new pain questionnaire. For the suggested new pain questionnaire, 11 existing pain descriptors were removed, 13 new descriptors were added to existing subclasses, and a new Psychological subclass comprising 9 descriptors was added. Conclusions: This study presents a novel methodology using social media data to identify new pain descriptors and can be repeated at regular intervals to ensure the relevance of pain questionnaires. The original MPQ contains several potentially outdated pain descriptors and is inadequate for reporting the psychological aspects of pain. Further research is needed to examine the reliability and validity of the revised MPQ. [ABSTRACT FROM AUTHOR]