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- eISSN
- 2444-8656
- Prima pubblicazione
- 01 Jan 2016
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- 2 volte all'anno
- Lingue
- Inglese
English for college students is one of the compulsory courses in university teaching, and its importance is reflected in all aspects, especially in conversation, and due to the limited English level of college students, various problems often occur in English conversation, which requires learning conversation repair; conversation self-repair is a self-corrective behaviour taken by the speaker after monitoring his or her own discourse errors, which is the main form of conversation repair [1–3]. Since 1977, when American sociologists Shegloff and Sacks [4] conducted a systematic study of conversational repair and first proposed the internal structure and judgment criteria of conversational self-repair, conversational self-repair has received the attention of many researchers at home and abroad. Over the past two decades, self-repair has achieved many results in the field of second language acquisition and other research areas. It is believed that self-repair can lead to comprehensible output, which is an important factor for successful language learning and a measure of success in second language learning.
Referring back is a common linguistic phenomenon that is frequently used in everyday conversation and writing. In order to meet the needs of communicative activities, language has developed a complete and complex system of reflexive language. We need to study not only the use of reflexive language in communication but also the structural features of the reflexive system itself. Specifically, the study of reflexives involves the following questions: Why do reflexives exist in their current structure? What are the roots of the differences between different reflexive structures and each other? How are reflexives used in discourse and what functions do they serve? How is the meaning of the reflexive interpreted in the process of communication? And so on. After the first mention of a person, thing or event in a discourse, we repeat the person, thing or event a second or third time in subsequent lines. This ‘‘‘repeated mention’’’ is a reflexive reference. The person, thing or event first mentioned exists in memory as a conceptual entity and becomes the object of the subsequent reflexive reference, which we call a prior concept. It is called a prior concept because it is already present in the context as opposed to the ‘‘‘repeated mention’’’ that follows. The word or phrase used in the first mention of the concept is called the ‘‘‘antecedent’’’, and it is the word that needs to be referred to in order to correctly interpret the reflexive that follows the repeated mention of the concept.
A reflexive is a word or phrase that is used to refer to a prior concept repeatedly in a discourse. The object referred to by the reflexive should be the same as the object referred to by the antecedent, i.e., it should be in a ‘‘‘homonymous’’’ relationship with the antecedent [5–10].
However, the questions of why there are structural and distributional differences between different reflexives and how they are interpreted in the communicative process are central to the study of reflexives. Different linguistic theories have attempted to answer these questions. As a typical linguistic phenomenon, it has become the best experimental object for linguistic schools to test the validity of their theoretical explanations. The explanation of the phenomenon of reflexivity depends fundamentally on our understanding of the nature of linguistic structure. Different linguistic theories have provided different explanations for the phenomenon from their own perspectives. Broadly speaking, there are three basic positions on the understanding of the nature of the reflexive phenomenon: formal, pragmatic and cognitive, and the corresponding representative theories are generative grammar, neo-Griceanism and accessibility theory, respectively [11–16].
Generative grammar has long dominated the field of language studies. The main researchers who have used the theory of generative grammar to study reflexivity are Reinhart (1983) and Chomsky (1981a, 1982). Chomsky’s theory of constraint attempts to find a set of explanatory rules for the internal referents of sentences. His study focuses on three types of referential phenomena: referring expression, anaphor and pronoun. Referring expressions include proper names, noun phrases, etc. (equivalent to what we call name words); corresponding expressions include reflexive pronouns, mutual pronouns, etc.; pronouns refer mainly to what is traditionally called personal pronouns in grammar. Like all other theories, Chomsky’s theory is based on a set of basic categories, and Chomsky’s syntactic theoretical system consists mainly of a series of conversion rules for the generation of small sentences. These rules are mainly based on a system of categories of sentence constituents and their relations. His study of reflexivity is also based on this purely syntactic system of categories.
Among the many studies, Pillai [17] proposed a model of the internal structure of self-repair that has greatly influenced the subsequent related studies. The model he developed is as follows (Figure 1):
Fig. 1
Self-repairing internal structure model
The above model shows that the talker self-monitors his cognitive speech behaviour, and when he detects a speech error after crossing ‘‘‘at the’’’, he stops the flow and enters the second phase of self-repair, i.e., the editing phase. In order to fill the gaps in this stage, the speaker used the filler word ‘‘‘no’’’. In this stage, he tries to re-plan the speech and speech repair strategies in order to repair the speech errors (hereinafter referred to as speech errors). Finally, in the third stage of repair, a replacement strategy was used, replacing ‘‘‘left’’’ with ‘‘‘right’’’. In the third stage of repair, the speaker did not repair the verbal error directly but redirected it to the beginning of the verb phrase, and the process went from self-repair to self-replanning to self-repair, where ‘‘‘turn’’’ was the ‘‘‘initial word of retraced constituent’’’ (hereinafter referred to as the initial word of retraced).
It was found that in the error correction stage of self-repair, the phenomenon of retracing is more common. The reflexive indicates that the speaker tends to go back to the beginning of the repaired constituent when performing the repair, rather than directly after the wrong word. This terminology was introduced by Levelt in 1983 in his model of the internal structure of self-repair described above and has been used ever since. A corpus study by Nootboom [18] found that lexical errors were more likely to cause reflexivity than phonological errors, but there is not much literature on the prominent phenomenon of verbal reflexive repair. Later, in a colour naming experiment with native speakers, the self-repair of speaker errors was divided into three types, with instant repair (starting the repair directly with the word at error) accounting for 42% and reflexive repair (anticipatory retracing: tracing to and repeating some word prior to the trouble element) accounting for 35%, and starting with fresh material that was not part of, Levelt (henceforth L1) argues that retracing indicates that the speaker is trying to help the listener with the problem of speech continuation because the listener is faced with the problem of distinguishing and identifying the word that the speaker is trying to express from the interrupted word, and thus, retracing helps the listener to relate the corrected word to the original word. Nagano [19] drew on Levelt’s experimental model to compare the difference in the use of verbal self-repair between L1 and second language learners (hereafter L2, Japanese learners of English in this study) and found that L2 used more reflexive repair than L1 under proper repair and wrong repair, with a frequency of 33%:25% and 41%:63%, respectively. He explained this phenomenon by the fact that L2 used finger-back repair to enable listeners to keep up with the communication; the speakers bought time to re-plan the language and used it as a delaying strategy.
A domestic researcher, Yaojianpeng [20], classified the strategies used by speakers for finger retrieval as retracing without overt correction (hereafter referred to as self-repetition), retracing with correction (which can be subdivided into replacement and insertion retracing) and retracing with reformulation (retracing with reformulation). The study found that function words were more likely to cause repetition than real words in self-repetition and that 70% of the retracing was caused by pronouns, i.e., retracing was most likely to be caused by function words. The study also pointed out that one of the motivations of the speaker for reflexive repair was to make the repaired speech accurate and syntactically and semantically acceptable. In addition, by referring back, the speaker expects the hearer to follow and understand his or her communicative intention. However, this study investigated the L1’s reflexive repair. The present study aims to investigate this common linguistic phenomenon in order to fill the gap. This study aims to answer the following two questions: (1) What are the characteristics of Chinese college students’ use of English conversational reflexive repair strategies and (2) What is the distribution of the refer-back words among Chinese college students in English conversational reflexive repair?
The corpus of this study was obtained from the College Learners Spoken English Corpus (hereinafter referred to as COLSEC) [21]. The source of the COLSEC corpus was the oral test of the National College English Test (CET-SET). The corpus is based on the transcribed materials of the CET-SET. The corpus was selected using a random proportional sampling method, proportional to the candidates’ regional origin, major, test scores and topics of conversation [22]. The types of discourse in the candidate corpus include three types: teacher-student interview, student discussion and teacher-student discussion, and all three parts of the examination sessions revolve around certain topics. A simple random sample of 31 texts was selected from 2000 to 2002, involving 29 male and 65 female examinees. There were 14 topics in the test, including mass media, the use of computer, examination, staying healthy, spending, big or small families and caring for others. The topics are divided into three main categories: campus life, social and natural concerns and personal and interpersonal issues, which have a strong focus and can easily reflect the characteristics of vocabulary use and the use of communication strategies.
In classifying the reflexive repair strategies of the COLSEC corpus, we drew on previous classification models and adjusted the classification model with the present corpus to classify the reflexive repair strategies into three major categories: the first category is reflexive without apparent error correction (i.e., self-repetition, hereafter referred to as repetition), which can be subdivided as follows: two-word repetition, multi-word repetition and continuous repetition (excluding word fragment repetition and one-word repetition which are common in self-repair because they do not have the characteristic of referring back). Two-word repetition is the repetition of two lexical items, which is the most common in this corpus. Multi-word repetition refers to repetitions involving more than two lexical items. There is a particular type of repetition in this corpus, where the speaker consistently makes a two-word or multi-word repetition. Researchers have interpreted repetition differently, and Hieke [23] states that repetition performs two main functions, namely, delay and repair. In addition, it acts as a pause, indicating that the speaker is assembling speech, during which the speaker’s memory is temporarily blocked, lexical extraction becomes difficult and time is needed to extract the exact word to overcome the current lexical selection problem [24]. The second category is error correction back to the finger, which includes substitution, insertion and deletion. Substitution is mainly accomplished through the speaker’s self-monitoring of lexical items, which suggests that the speaker’s motivation for this repair is to use more appropriate lexical and grammatical expressions to convey his or her communicative intentions. The insertion not only indicates a high level of self-monitoring ability and awareness of natural speech communication but also clearly indicates that the discourse is made more complete and accurate by inserting information to express the discourse’s communicative intentions. Deletion is a new type found in this corpus. In this type of reflexive, the discourse participant undergoes a reflexive-repair process when performing substitution, insertion or deletion repair. The third category is reorganisation: in this type of repair, the speaker refers back to the beginning of the constituent, but after referring back to the word, he or she re-plans the discourse in terms of syntactic structure or semantic composition. We manually annotated the corpus according to the above classification model and then counted the frequency of each type of repair using the search function of corpus retrieval software AntConc 3.2.2.
However, the classification of reflexives can be multifaceted: we can classify them by their formal structural features, by their syntactic function or by the conceptual relationship between the reflexive and its antecedent. In a broad sense, all repeated references to people, things, features, behaviours, etc. in a discourse can be regarded as reflexive. In sentences, these different referred objects often belong to different semantic roles or event elements of the sentence proposition and correspond to different sentence constituents. For example, people and things often correspond to nouns, features often correspond to adjectives or adverbs and actions often correspond to verbs. Therefore, some scholars classify reflexives according to the syntactic features of the antecedent.
Classification methods are based on context-sensitive contexts and can be traced back to coronal and prepositional error correction studies in the early days. In the classification approach, for a specific syntactic error type, the classification model eventually outputs a label representing the correct form of the target word (e.g., on the singular-plural type of a noun, 0 for the singular form and 1 for the plural form). Classification models are divided into two main categories: statistical classification models and deep classification models [25].
Statistical classification models are more common in GEC research before the recovery of deep learning and involve models such as average perceptual machines, plain Bayesian classifiers and N-gram-based language models [26, 27].
The perceptron is a linear classification model that works by classifying an input feature vector [28] into two classes (+1 or –1) by means of a linear discriminant function, specifically the linear discriminant function has the following mathematical Eq. (1):
Linear Eq. (3) represents a hyperplane on the feature space
There is also the plain Bayesian classifier, a classification model based on Bayes’ theorem [29], which takes the category with the highest probability as the category of the item to be classified by finding the probability of occurrence of each category given the conditions of occurrence of the item to be classified. Bayes’ theorem is able to calculate the conditional probability P(B∣A) of event B occurring on the basis of knowing the probability P(A) of event A occurring, the probability P(B) of event B occurring and the conditional probability P(A∣B) of event A occurring on the basis of B occurring, as in Eq. (5):
Suppose Eq. (6) is a term to be classified, where
N-gram is a natural language processing model based on a statistical approach [30], and its core principle is to process text using a sliding window of length N bytes to produce a sequence of byte fragments of length N. Each byte fragment is called a gram. The algorithm counts the self-count of all the grams that appear in the text, and it then filters out the grams with low occurrences according to a certain threshold, thus obtaining a list of key grams representing the feature space of the text vector, where each gram in the list represents one feature vector dimension of the space.
The N-gram assumes that the occurrence probability of the Nth word is related to the previous N-1 words only, and the occurrence probability of the whole sentence is the product of the occurrence probabilities of individual words. At present, the most commonly used N-grams are binary gram and ternary gram.
N-grams can be used to evaluate whether an utterance is reasonable or not. Suppose there is a sequence of m words and we want to get the probability
Using the Markov chain assumption that the current word is only related to a limited number of words before it, the computational effort can be significantly reduced. The specific formula is as follows (10):
The common binary and ternary models are defined as Eqs (11) and (12), respectively:
Therefore, given the training corpus, the above conditional probabilities can be computed using Bayes’ theorem. When selecting the more correct sentence among the two corrected sentences, the N-gram can be used to calculate the probabilities of the two sentences separately, and the one with the higher probability is selected as the final correct sentence.
This paper presents important research results in the field of English grammar error correction in recent years and research works that are closely related to the algorithms in this paper. These research studies mainly include word embedding and Seq2Seq models.
Word embedding method is to map each word or phrase from a high-dimensional space with dimensionality of all the number of words to a low-dimensional real domain space, where each word or phrase corresponds to a vector in this low-dimensional space. Word2Vec and GloVe are two excellent algorithms for generating word embedding vectors.
Word2Vec learns the vector representation of words from a corpus in an unsupervised way, including two models, CBOW and Skip-gram, assuming a window size of c. A word
GloVe is an unsupervised machine learning algorithm that trains word vectors based on global word frequency statistics. Similar to the Word2Vec method, GloVe word vectors can also obtain some semantic properties between words, such as similarity and analogy. GloVe is implemented in three steps as follows: Construct a co-occurrence matrix X based on the corpus, where each element Construct the approximate relationship between the word vector and the co-occurrence matrix, and the formula is Eq. (14): Construct a loss function based on the mean variance as in Eq. (15): Here, the Compared with Word2Vec, GloVe is easier to parallelise during the training process and therefore faster to train on large data sets. In practice, GloVec word vectors perform better on test sets for some tasks, and word2Vec word vectors perform better on test sets for other tasks. Overall, there are no clear advantages or disadvantages of these two methods, and both are the preferred word embedding methods today.
The Seq2Seq model was first applied to neural machine translation research. The Seq2Seq model first obtains the vector representation of each word in the input sentence by word embedding and then inputs the vector sequence
The RNN unit in the Seq2Seq model is designed to evaluate the conditional probability, as in Eq. (17):
The Seq2Seq model in English grammar error correction is basically the same as that in machine translation, except that the parallel corpus is no longer one country’s language corresponding to another country’s language, but sentences with grammatical errors corresponding to correct sentences. Various improvement and optimisation methods for the Seq2Seq model are applicable to both scenarios.
We counted the refer-back words (same method as above) and compared them with the distribution of refer-back words in the Santa Barbara American Spoken English Corpus Linguistic Data Consortium (LDC) 2003S06 from Yaojianpeng [20] (Table 1).
Distribution of anaphora in corpus COLSEC
| Function word | Pronoun | 120 | 45.2 |
| Preposition | 20 | 9.1 | |
| Conjunction | 25 | 11.2 | |
| In total | 165 | 65.5 | |
| Notional words | Noun | 4 | 2.1 |
| Verb | 40 | 19.2 | |
| Adjective | 9 | 4.3 | |
| Adverb | 5 | 2.2 | |
| In total | 58 | 27.8 |
From the previous discussion, it can be seen that Chinese college students used the most repetition among the finger-back repair strategies, among which two-word repetition, multi-word repetition and continuous repetition were dominant, which is basically consistent with the related studies at home and abroad. The majority of error-correcting reflexes were substitution, insertion, and deletion, and students showed a more flexible level of substitution, while only a small proportion of reorganisation reflexes were used. We suggest that these characteristics of students’ use of finger-repair strategies are influenced by their self-monitoring ability, information processing ability and attention allocation in verbal communication.
Self-monitoring is essential for self-repair of verbal errors, and in natural speech monitoring theory, perceptual circuit theory is involved in this process. In this context, the speech production model consists of four modules (conceptualiser, formulator, articulator and self-monitoring), two loops (internal loop and external loop perceptual loops) and a speech comprehension system (see Figure 2), in which the speaker self-monitors his or her speech through two perceptual loops. The inner loop monitors the information between the syntactic formers and the concept formers through the speech understanding system, while the outer loop monitors the information between the articulators and the concept formers through the listening and speech understanding system. In this way, not only implicit speech errors are monitored but also explicit speech errors are monitored so that the communication between the speaker and the listener can be carried out smoothly and accurately.
Fig. 2
Speech production model
Self-monitoring is closely related to controlled processing and attention, and since human attention is limited, if attention is not sufficient to perform the task, language use will be affected, as well as the efficiency of the monitoring process and the number and type of errors noticed by the speaker. Also, according to our research, the speaker’s attention allocated is related to the context and the task, and the speaker may only pay attention to certain errors or disfluencies and ignore others. In cognitive psychology, attention is seen as an internal mechanism that enables control of stimulus selection and regulates behaviour, i.e., discarding some information in order to process important information efficiently. Then there are two modes of information processing in attention theory: controlled processing and automatic processing, which are able to control the correction of college students’ English fingerspelling. However, controlled processing is a kind of processing that requires the application of attention and has a limited capacity, which is also called attentional processing; automatic processing is a kind of processing that is not controlled by human beings, which does not require the application of attention and does not have a certain capacity to control, and once formed, it is difficult to change. When applied to second language learning, it can be understood that learners have a limited capacity to process information. Therefore, to master a new language, one can only learn its sub-skills gradually and then connect each sub-skill in series and finally reach the level of proficiency in the whole skill, which is the process from controlled processing to automatic processing, and finally to the gradual internalisation of the learner’s language knowledge.
Our research shows that the most important reason why L2 monitoring is different from that of the native language is that L2 lexical, syntactic and phonological processing requires attentional capacity, which is limited. In the process of bilingual production, speakers have limited knowledge of the target language and encounter difficulties in extracting appropriate lexical items and syntactic structures, and the encoding of phonological plan components is far from automatic; so, they cannot express their thoughts coherently, which inevitably leads to many speech errors. Therefore, the limited attentional resources of the speakers can only focus on monitoring and self-repairing those linguistic components that may affect the accuracy of information, while preserving those errors that do not affect the accuracy of information. Take the substitutions ‘‘‘And you know, many big companies er make big deals through the Internet. There is a very fashion, is a very fashionable word(s) called commercial business.’’’ for example that occur more frequently in this corpus. This example is part of the candidate’s answer to the examiner’s question about the negative impact of computers. The candidate clearly had difficulty in extracting the word ‘‘‘fashionable’’’ and the concept ‘‘‘commercial business’’’, and his limited attention was focused on monitoring the language components that might have affected the accuracy of the information. The limited attention was focused on the monitoring of linguistic components that might affect the accuracy of the information and then stalled for time by repeating ‘‘‘is a very’’’ to restore the memory to its cognitive resources, finally replacing the wrong word ‘‘‘fashion’’’ with ‘‘‘fashionable’’’, while those errors that did not affect the accuracy of the information, such as ‘‘‘a very fashionable words’’’, were retained. The above example shows that when fixing a complete error of speech, the speaker tends to keep the syntax of the original speech in the repair, repeating a part of the original speech and fixing the part that needs to be replaced. However, when the error is merely incomplete, the speaker often reconstructs it, either by inserting new materials into the original discourse, using an insertion strategy, or by choosing to suspend the flow of discourse online and re-plan the discourse, using a restructuring strategy. These two repair strategies require a high level of information processing and online language processing skills.
English education for college students is of great developmental importance; this study analysed the patterns and characteristics of the finger-back repair and the characteristics of the use of finger-back words among Chinese college students and came to the following conclusions: (1) The frequency of finger-back repair is high, reflecting that college students have a strong sense of communicative interaction and prompt the listener to solve the problem of topic connection through finger-back repair; (2) Students’ excessive use of certain double-word or multi-word repetition gives a ‘‘‘self-centered’’’ (I perspective) perspective and often gives listeners a monotonous and inauthentic feeling, which may be due to the influence of their native language; and (3) The proportion of self-repetition is the highest among the college students, reaching about 75%, and the proportion of reorganisation and insertion strategies is very small, about 23%, which reflects the characteristics of college students’ ability in linguistic information processing and online processing. The present study aims to make an indicative role for the subsequent in-depth investigation of the function of verbal finger-referential repair.
Fig. 1
Fig. 2
Distribution of anaphora in corpus COLSEC
| Function word | Pronoun | 120 | 45.2 |
| Preposition | 20 | 9.1 | |
| Conjunction | 25 | 11.2 | |
| In total | 165 | 65.5 | |
| Notional words | Noun | 4 | 2.1 |
| Verb | 40 | 19.2 | |
| Adjective | 9 | 4.3 | |
| Adverb | 5 | 2.2 | |
| In total | 58 | 27.8 |
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sensitive random demand Limit cycles of a generalised Mathieu differential system Influence of displacement ventilation on the distribution of pollutant concentrations in livestock housing Application of data mining in basketball statistics The nonlinear effects of ageing on national savings rate – An Empirical Study based on threshold model Design of fitness walker for the elderly based on ergonomic SAPAD model AtanK-A New SVM Kernel for Classification Mechanical behaviour of continuous girder bridge with corrugated steel webs constructed by RW Study of a linear-physical-programming-based approach for web service selection under uncertain service quality The Relationship Between College Students’ Taekwondo Courses and College Health Based on Mathematical Statistics Equations Analysis and countermeasures of cultivating independent learning ability in colleges teaching English based on OBE theory A mathematical model of plasmid-carried antibiotic resistance transmission in two types of 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in Programmable Networks The Complexity of Virtual Reality Technology in the Simulation and Modeling of Civil Mathematical Models University Library Lending System Model Based on Fractional Differential Equations Calculation and Performance Evaluation of Text Similarity Based on Strong Classification Features Intelligent Matching System of Clauses in International Investment Arbitration Cases Based on Big Data Statistical Model Evaluation and Verification of Patent Value Based on Combination Forecasting Model Financial Institution Prevention Financial Risk Monitoring System Under the Fusion of Partial Differential Equations Prediction and Analysis of ChiNext Stock Price Based on Linear and Non-linear Composite Model Calculus Logic Function in Tax Risk Avoidance in Different Stages of Enterprises The Psychological Memory Forgetting Model Based on the Analysis of Linear Differential Equations Optimization Simulation System of University Science Education Based on Finite Differential 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Index Evaluation Based on Logistic Distribution Fitting Transition Probability Function Children's Educational Curriculum Evaluation Management System in Mathematical Equation Model Query Translation Optimization and Mathematical Modeling for English-Chinese Cross-Language Information Retrieval The Effect of Children’s Innovative Education Courses Based on Fractional Differential Equations Fractional Differential Equations in the Standard Construction Model of the Educational Application of the Internet of Things Optimization research on prefabricated concrete frame buildings based on the dynamic equation of eccentric structure and horizontal-torsional coupling Optimization in Mathematics Modeling and Processing of New Type Silicate Glass Ceramics Green building considering image processing technology combined with CFD numerical simulation Research on identifying psychological health problems of college students by logistic regression model based on data mining Abnormal Behavior of 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Function Model Design of Tennis Mobile Teaching Assistant System Based on Ordinary Differential Equations Financial Crisis Early Warning Model of Listed Companies Based on Fisher Linear Discriminant Analysis High Simulation Reconstruction of Crowd Animation Based on Optical Flow Constraint Equation Construction of Intelligent Search Engine for Big Data Multimedia Resource Subjects Based on Partial Least Squares Structural Equation 3D Animation Simulation of Computer Fractal and Fractal Technology Combined with Diamond-Square Algorithm Analysis of the Teaching Quality of Physical Education Class by Using the Method of Gradient Difference The Summation of Series Based on the Laplace Transformation Method in Mathematics Teaching Optimal Solution of the Fractional Differential Equation to Solve the Bending Performance Test of Corroded Reinforced Concrete Beams under Prestressed Fatigue Load Animation VR scene mosaic modeling based on generalized Laplacian equation Radial Basis Function 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points based on machine learning Analysis of higher education management strategy based on entropy and dissipative structure theory Prediction of corporate financial distress based on digital signal processing and multiple regression analysis Mathematical Method to Construct the Linear Programming of Football Training Multimedia sensor image detection based on constrained underdetermined equation The Size of Children's Strollers of Different Ages Based on Ergonomic Mathematics Design Application of Numerical Computation of Partial Differential Equations in Interactive Design of Virtual Reality Media Stiffness Calculation of Gear Hydraulic System Based on the Modeling of Nonlinear Dynamics Differential Equations in the Progressive Method Knowledge Analysis of Charged Particle Motion in Uniform Electromagnetic Field Based on Maxwell Equation Relationship Between Enterprise Talent Management and Performance Based on the Structural Equation Model Method Term structure of economic management rate based on parameter analysis of estimation model of ordinary differential equation Influence analysis of piano music immersion virtual reality cooperation based on mapping equation Chinese painting and calligraphy image recognition technology based on pseudo linear directional diffusion equation Label big data compression in Internet of things based on piecewise linear regression Animation character recognition and character intelligence analysis based on semantic ontology and Poisson equation Design of language assisted learning model and online learning system under the background of artificial intelligence Study on the influence of adolescent smoking on physical training vital capacity in eastern coastal areas Application of machine learning in stock selection Comparative analysis of CR of ideological and political education in different regions based on improved fuzzy clustering Action of Aut( G ) on the set of maximal subgroups ofp -groupsStudy on transmission characteristics in three kinds of deformed finlines based on edge-based finite element method Research on loyalty prediction of e-commerce customer based on data mining Algebraic Equations in Educational Model of College Physical Education Course Education Professional English Translation Corpus Under the Binomial Theorem Coefficient Geometric Tolerance Control Method for Precision Machinery Based on Image Modeling and Novel Saturation Function Retrieval and Characteristic Analysis of Multimedia Tester Based on Bragg Equation Semiparametric Spatial Econometric Analysis of Household Consumption Based on Ordinary Linear Regression Model Video adaptive watermark embedding and detection algorithm based on phase function equation English Learning Motivation of College Students Based on probability Distribution Scientific Model of Vocational Education Teaching Method in Differential Nonlinearity Research on mobile Awareness service and data privacy Protection based on Linear Equations computing protocol Vocal Music Teaching Model Based on Finite Element Differential Mathematical Equations Studying a matching method combining distance proximity and buffer constraints The trend and influence of media information Propagation based on nonlinear Differential equation Research on the construction of early warning model of customer churn on e-commerce platform Evaluation and prediction of regional human capital based on optimised BP neural network Study on inefficient land use determination method for cities and towns from a city examination perspective A sentiment analysis method based on bidirectional long short-term memory networks A study of local smoothness-informed convolutional neural network models for image inpainting Mathematical Calculus Modeling in Improving the Teaching Performance of Shot Put Application of Nonlinear Differential Equation in Electric Automation Control System Higher Mathematics Teaching Curriculum Model Based on Lagrangian Mathematical Model Computational Algorithm to Solve Two–Body Problem Using Power Series in Geocentric System Decisions of competing supply chain with altruistic retailer under risk aversion Optimization of Color Matching Technology in Cultural Industry by Fractional Differential Equations The Marketing of Cross-border E-commerce Enterprises in Foreign Trade Based on the Statistics of Mathematical Probability Theory Application of Linear Partial Differential Equation Theory in Guiding Football Scientific Training Nonlinear Channel Estimation for Internet of Vehicles Some Necessary Conditions for Feedback Functions of de Bruijn Sequences The Evolution Model of Regional Tourism Economic Development Difference Based on Spatial Variation Function System Model of Shipping Enterprise Safety Culture Based on Dynamic Calculation Matrix Model An empirical research on economic growth from industrial structure optimisation in the Three Gorges Reservoir area The Inner Relationship between Students' Psychological Factors and Physical Exercise Based on Structural Equation Model (SEM) Analysis and Research on Influencing Factors of Ideological and Political Education Teaching Effectiveness Based on Linear Equation Study of agricultural finance policy information extraction based on ELECTRA-BiLSTM-CRF Fractional Differential Equations in Sports Training in Universities Examination and Countermeasures of Network Education in Colleges and Universities Based on Ordinary Differential Equation Model Innovative research of vertical video creation under the background of mobile communication Higher Education Agglomeration Promoting Innovation and Entrepreneurship Based on Spatial Dubin Model Chinese-English Contrastive Translation System Based on Lagrangian Search Mathematical Algorithm Model Genetic algorithm-based congestion control optimisation for mobile data network