# A Corpus-Based and Fuzzy Assessment-Based Study of Labels in Spoken English

Authors: Wei Jiang
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

Label English is common in spoken English, but for most English learners, it is difficult to use and express labels correctly and appropriately. This is mainly due to their lack of understanding of this expression. Label English is equivalent to Chinese grammar, using different markup components to form a complete sentence. In this paper, five tags commonly used by native English speakers, namely, or something, or something, and so on, and that, and everything, will be studied. Through the method of fuzzy evaluation, it illustrates the use of labels by Chinese English learners and the similarities and differences between Chinese English learners and native English speakers. Corpus is a product of language research and corresponding computer technology. It is a model of the combination of quantitative and qualitative methods in language research. It makes its due contribution to revealing the essence of language and can provide scientific basis for language research. Based on the above background, the purpose of this paper is to study tags in spoken English through corpus and vague evaluation. And the top five tags are retrieved in CLEC and LOB. Then, the frequency of occurrence is counted. Finally, the variance of each label is calculated by using the method of fuzzy evaluation, to determine whether there are significant differences between the two corpuses. The fuzzy evaluation method can make the results clear and systematic, can better solve the vague and difficult to quantify problems, and is suitable for solving various non-deterministic problems. The results show that besides “and so on,” Chinese English learners use labels less frequently than native English speakers. In terms of structure, both of them adopt the prototype of tags, but most Chinese learners misuse some of their own tags. The main reasons for this phenomenon are mother tongue transfer and inadequate pragmatic competence. In contrast, native English speakers use tags more frequently. In terms of usage form, the prototypes of the above five tags are used, but there are also variants, mainly in the form of expansion and lexical variants. The 106 tagged words found in CLEC were searched one by one in COLSEC. It was found that only 19 expressions appeared in the corpus, with a total frequency of only 271 and a standard frequency of 371. The total frequency of this type of expression in LOB is 11072 times, and the standard frequency is 1055 times. In addition, these expressions also have the pragmatic functions of inference, reinforcement, and compliance.


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## Body

## 1. Introduction

One of the most important characteristics of human language is its productivity, which means that language users can manipulate a limited set of linguistic rules to create unlimited new expressions and sentences [1, 2]. In a sense, this is a fact, but it is not the case. Over the past few decades, more and more linguists have turned their attention from the productive aspects of language to the conventional ones. Many linguists have conducted some research to study language use [3]. In addition, a new discipline has emerged in the field of linguistics, which makes dictionaries more and more important [4]. The oral English of the student number can get rid of the traditional Chinese English to a certain extent and make the communication smoother. While congratulating ourselves on our achievements so far, we also found that there is still much room for improvement. Although Chinese learners have received high marks in various English exams and exams, they still have fatal weaknesses: pragmatic awareness and communication strategies. By analyzing the use of Chinese language learners’ linguistic expressions (ambiguous labels with native speakers), the study may give rise to some thoughts on the issue and hopefully provide somebody interested in the topic. Some inspiration [5].Over the past few decades, interest in the practical and communicative strategies of language users has grown, and researchers have focused their attention on the expression of a large number of fuzzy definitions [6–8]. These expressions are called “discourse particles,” “pragmatic particles,” and “pragmatic operators” [9]. As part of this collection, fuzziness tags have multiple functions in language use, especially in spoken language interactions [10]. A good expression of this set of expressions can show the user’s communication ability and fluency [11, 12]. At present, fuzzy labels have attracted widespread attention in linguistic research [13, 14]. Much research has been done from various aspects. Unfortunately, in China, there is little attention to this issue. This is a big gap in Chinese English learning [15]. As a result, various corpora have been published and related products throughout the country. In particular, we should pay attention to the two learner corpora established in China: CLEC (Chinese Learner English Corpus) and COLSEC (College English Learners’ Spoken English Corpus), which greatly promotes the intermediate language of corpus-based Chinese learners in all aspects of learning [16].The purpose of this paper is to study the label language in spoken English through corpus and fuzzy evaluation and to search for the top five fuzzy label words in CLEC and LOB. Then, count the frequency of occurrence. Finally, the fuzzy evaluation method is used to calculate the variance value of each label language to determine whether there is a significant difference between the above three corpora. The results show that in addition to “and so on,” Chinese English learners use fuzzy labeling less frequently than native speakers. In terms of the structure used, both use the prototype of the label language, but most Chinese will learn. The person will misuse and create some label language. The main reason for this phenomenon is the lack of mother tongue transfer and pragmatic ability. In contrast, native speakers use a higher frequency of fuzzy slogans. The prototypes of the above five fuzzy tagged words are used in the form of use, but there are variants, mainly implemented in the form of extensions, lexical variants, and the like. In addition, such expressions have pragmatic functions such as inference, reinforcement, and compliance.This article is divided into five chapters. The first chapter briefly introduces the label language in English spoken language, the formula expression of fuzzy evaluation, and corpus linguistics as the background knowledge of research.In the same chapter, the research objectives and issues are specified. The second chapter is a literature review, which will review previous studies on this issue.The third part will conduct an exploratory study of the use of labels by native speakers. First, the method that will be used in the study will be defined: the corpus to be used in the study will be introduced; the main procedures will be developed. After sorting out what previous scholars have done on this issue, we will begin our own exploration: extracting data from CLEC and LOB corpora, finding out all the ambiguous labels that appear in the corpus and counting the frequencies, count it, and analyze forms and functional characteristics [17, 18].The fourth part is a comparative study on the use of ambiguous labels between native English speakers and Chinese English learners. The difference in quantity was discovered, and some formal and practical traits in Chinese learners’ English were discovered and discussed the reasons for the difference.The fifth chapter is the conclusion part, which puts forward the teaching significance of the research and points out the limitations.


## 2. Proposed Method

### 2.1. Related Work

Chinese students are the largest international student group in UK universities today, but little is known about their undergraduate writing and challenges. Utilizing the British Academic Written English Corpus, a large number of skilled undergraduate writing corpora are collected in the UK in the early 2000s. The Wingate studied explores the written assignments written by Chinese students in a series of university subjects in English and compares them with the work of British students. The study was complemented by questionnaires and interview data sets consisting of subject lecturers, writing mentors, and students, providing a comprehensive picture of today’s Chinese student writers. In theory, through the work framework initiated by academic knowledge and vocabulary, Wingate tried to explore the writing of Chinese students we know and extends these findings more broadly to undergraduate writing. In a globalized educational environment, it is important that educators understand the differences in student writing styles and move from a ubiquitous student underwriting model to a descriptive model that encompasses different approaches to success. English is of great value to researchers, EAP tutors, and university lecturers teaching Chinese students in the UK, China, and other English or Chinese speaking countries [19]. The bilingual parallel corpus provides a rich source of translation information for tasks such as crosslanguage information retrieval and data-driven machine translation systems. However, they are often scarce resources: size, language coverage, and language registration are limited. Researchers must work to adapt and adapt to the available technologies, because only a few small corpora are appropriate. Tejada introduces a large Chinese-English parallel corpus built by retrieving and processing bilingual web documents on the Internet. Currently, it contains approximately 300,000 parallel sentence pairs. It also describes the tools and methods used in this collection project. In crosslanguage retrieval tasks, Tejada attempted to use this self-constructed corpus to improve the quality of query translation for better retrieval performance [20]. The sporting performance of an autonomous underwater vehicle (AUV) is critical to the safety and measurement accuracy of the AUV. However, the relationship between the metrics used to assess athletic performance is often complex and cannot be expressed in mathematical formulas, and it is difficult to figure out how each metric affects the athletic performance of the AUV. Liu proposed a fuzzy comprehensive evaluation (FCE) method to evaluate the motion performance of underwater robots. For the landing underwater vehicle, the process of the FCE method is described in detail, and the FCE system is constructed. In the FCE system, a three-level evaluation index system was established based on the motion characteristics of the landing AUV [21]. Based on the analysis of the investigation process and measurement requirements of the landing underwater vehicle, the fuzzy analytic hierarchy process is used to determine the weight set of each factor set. A one-factor evaluation matrix is obtained by solving a membership function having a ridge distribution. The decision is made by comparing the results of the athletic performance evaluation of the two layout schemes. Field tests show that the evaluation results can objectively and comprehensively reflect the motion performance of the landing underwater robot [22]. The comprehensive strength of a company’s patents is a key factor in measuring its core competitiveness. It is of strategic significance to conduct objective analysis and evaluation of the comprehensive patent strength of enterprises. On the basis of domestic and foreign literature research, case studies, and expert consultation, according to the four aspects of patent creation, application, protection, and management, it initially constitutes the overall evaluation index system of enterprise patent comprehensive strength. It consists of 16 indicators. Through fuzzy comprehensive evaluation and case analysis, it analyzes the evaluation model and method of enterprise patent comprehensive strength. Xu and Song believed that the assessment is essentially multiobjective, multifactor, multilayered, vague, and comprehensive, emphasizing that the evaluation of the indicator system and the rational selection of assessment methods are essential for the comprehensive evaluation of corporate patents. Systematic, comprehensive, and scientific effectively enhance the core competitiveness of enterprises and achieve sustainable development of enterprises [23]. In the field of second language acquisition, the mother tongue has both positive and negative transfer. Appropriate foreign language learning strategies can effectively avoid the negative transfer of mother tongue and improve learners’ oral English ability. Based on the theory of language transfer, Sadeghi focused on error analysis and interlanguage theory. In recent years, in the field of college English teaching, more and more scholars have studied the migration of mother tongue, and the main points are different. However, little research has been done on the impact of mother-tongue transfer on the oral English of non-English majors. This case study selected non-English majors as subjects and analyzed the reasons for oral English errors and language categories. The motivation for the student to learn spoken English is to participate in the graduate oral exam and TOEFL. The subject was asked to take two oral and one interview. Through the analysis and research of experimental data, Sadeghi found that students’ oral English errors are mainly affected by the negative transfer of negative mother tongue and negative cultural migration. The errors in spoken language are mainly reflected in speech, vocabulary, grammar, and pragmatics. Sadeghi will focus on three areas: vocabulary, grammar, and pragmatics. The results also show that the most difficult problem for non-English majors in the English oral process is the lack of vocabulary. It is therefore difficult to express their ideas in English [24].


### 2.2. Introduction to The Corpus

A corpus is actually a collection of linguistic materials that are collected in electronic form and stored in electronic form. These materials are mainly derived from naturally collected corpora. At present, there are many large corpora in China for us to study and analyze, but the larger the corpus may have more corpus sources, the weaker the relevance of some research. For the above reasons and research purposes, we have built a small corpus. In this paper, we mainly study label language in spoken English; so, we need a lot of first-hand English spoken language learners to quantify the grammar of the tagged words. Therefore, the biased corpus of spoken English in Chinese students in the “HSK” dynamic spoken English corpus was also selected as the data analysis.HSK is a standardized international Chinese proficiency test established to test the Chinese proficiency of nonnative Chinese speakers. It evaluates the Chinese proficiency of the test subjects. A small corpus was established with the first-hand corpus of the collected spoken English learners for follow-up, used in analytical studies.CLEC: (Chinese Learner English Corpus) contains an English database of about 1 million English words collected from English-speaking learners. It has a layered English language proficiency, covering high school, junior high school, Elementary school, and Chinese university students who are not English majors and English majors. The material in the corpus is not sufficient to compare between different levels, but it is still a reasonable resource for observing the learning of English-speaking learners in Chinese EFL.LOB: the LOB corpus is a British English version of the English corpus. It contains 500 texts in spoken English, 2000 words, distributed in 15 text categories, 9 of which are informative and 6 imaginative. There are two versions of the LOB corpus: the original version and the version with the POS tag.


### 2.3. Introduction to the Fuzzy Evaluation Method

When evaluating objective things, no matter how precise the words are, the evaluation results are still far from the things themselves. Just as two people see the same thing is beautiful, the degree of beauty of the objects they evaluate is obviously different. In fact, this is because of the characteristics of the language itself. China’s philosophers have long discovered that although the language tool is conducive to communication, it is inherently ambiguous, but the pursuit of things is really the driving force of human beings and the pursuit of true knowledge of things. It is said that beauty is ugly. There is no ugly beauty. It is obvious that this dialectical unity can only open up new ideas for understanding the world, but it is not conducive to more accurate evaluation of things. Buddhism says that everyone has the ability to know the true colors of the world, but the inner heart is covered by some dust in front of them. For a more accurate evaluation of things, it is advocated to feel with the mind. Obviously, the Eastern world has not proposed a more quantitative method of evaluating things. However, the rational thinking of Westerners thinks of using mathematical methods to define some fuzzy evaluations, which is also the reason for fuzzy mathematics.Use fuzzy mathematics to solve some fuzzy phenomena. It is obviously a big innovation to use the accuracy of mathematics to deal with the ambiguity of the world itself. Using this idea, the use of fuzzy mathematics has now expanded from dealing with fuzzy phenomena to more complex and broad disciplines, just as this paper wants to use fuzzy mathematics to solve some of the risk problems in business operations. Fuzzy evaluation deals with the fuzzy evaluation objects by precise digital means and can make a more scientific, reasonable, and practical quantitative evaluation of the data with ambiguity in the information. The evaluation result is a vector, not a point value, and the information contained Rich, and it can not only describe the object being evaluated more accurately but also can be further processed to obtain reference information.


### 2.4. Evaluation Procedure Based on the Fuzzy Evaluation Method

The use of the fuzzy evaluation method for the study of label language in spoken English includes four main steps: one is to determine the set of factors, and the abovementioned indicators through the test are used as factors to build the set in order; the second is to give a set of comments, according to each indicator according to its own. The characteristics and feasible range are set to the evaluation level, and each level has a specific description. The third is to evaluate the degree of each factor, establish a fuzzy relationship matrix, and calculate the weight of the index according to the matrix score; the fourth is to determine the degree of prominent influence. VectorΛ is a computational judgment matrix and nonlinear fuzzy comprehensive evaluation.


#### 2.4.1. Determine the Factor Set

All evaluation indicators are numbered sequentially, and a set of indicators for evaluation is set up, denoted byU.
(1)U=u1,u2u3,⋯,un.U=u1,u2u3,⋯,un is the evaluation index, which describes the nature of the evaluation object from different angles, and has different degrees of ambiguity.
(1)
Give a comment set to establish an evaluation level for each indicator according to its scope. Each level of the indicator has its own comment description, and all the indicator level reviews are set up with a comment set, indicated byV(2)V=v1,v2,v3,⋯,vm(2)
The fuzzy relationship matrix and weight vector are determined by the single factor evaluation methodThe individual indicators are evaluated, and the weights of each indicator are calculated by mathematical methods (the analytic hierarchy process is used in this paper) to establish a fuzzy model, that is, the evaluation resultVi of the single factor Ui, and the weight vector R can be expressed as
(3)Ri=ri1,ri2,ri3,⋯,rij.Analytic hierarchy process takes the research object as a system and makes decisions according to the thinking method of decomposition, comparison and judgment, and synthesis. The degree of influence of each factor on the results is quantified, very clear, and clear. This method is especially useful for systematic evaluations of unstructured properties and multiobjective, multicriteria, multiperiod, etc. systematic evaluations.(3)
Determine the degree of influence of the indicator vectorΛ and calculate the final evaluation result. Evaluate the degree of influence of each indicator on performance. The greater the degree of influence, the larger λi, usually taking integers. This paper adopts the “five-scale” method, and the value is 1 ~ 5; if the evaluation index does not have a prominent influence, λi=1, and let λ=max (λ1, λ2, λn), the nonlinear fuzzy matrix synthesis operator can be defined as(4)fa1,a2,⋯,an;x1,x2,⋯,xn;Λ=a1x1λ1+a2x2λ2+⋯+anxnλn12.AssumeA=a1,a2,⋯an, ai>0, and ∑ai=1,(5)X=x1,x2,⋯xn,xi≥1.The vector operator that highlights the degree of influence is represented byΛ=λ1,λ2,⋯λn,xi≥1, where λi≥1.


#### 2.4.2. Fuzzy Comprehensive Evaluation

According to the comprehensive evaluation result and the principle of maximum membership degree, the maximum membership principle is one of the basic principles of fuzzy mathematics. It is a direct method for model identification using fuzzy set theory. Forn actual models, it can be expressed as n fuzzy subsets A1 and A2 on the universe X, ..., An, and x0∈X is a specific identification object, if there is i≤n, and Aix=maxA, then x is said to belong to A relatively, which is the principle of maximum membership. The elements corresponding to the final evaluation result and the maximum evaluation index in the evaluation set are taken as the final evaluation result, that is, bj=maxb1,b2,⋯bn.


## 2.1. Related Work

Chinese students are the largest international student group in UK universities today, but little is known about their undergraduate writing and challenges. Utilizing the British Academic Written English Corpus, a large number of skilled undergraduate writing corpora are collected in the UK in the early 2000s. The Wingate studied explores the written assignments written by Chinese students in a series of university subjects in English and compares them with the work of British students. The study was complemented by questionnaires and interview data sets consisting of subject lecturers, writing mentors, and students, providing a comprehensive picture of today’s Chinese student writers. In theory, through the work framework initiated by academic knowledge and vocabulary, Wingate tried to explore the writing of Chinese students we know and extends these findings more broadly to undergraduate writing. In a globalized educational environment, it is important that educators understand the differences in student writing styles and move from a ubiquitous student underwriting model to a descriptive model that encompasses different approaches to success. English is of great value to researchers, EAP tutors, and university lecturers teaching Chinese students in the UK, China, and other English or Chinese speaking countries [19]. The bilingual parallel corpus provides a rich source of translation information for tasks such as crosslanguage information retrieval and data-driven machine translation systems. However, they are often scarce resources: size, language coverage, and language registration are limited. Researchers must work to adapt and adapt to the available technologies, because only a few small corpora are appropriate. Tejada introduces a large Chinese-English parallel corpus built by retrieving and processing bilingual web documents on the Internet. Currently, it contains approximately 300,000 parallel sentence pairs. It also describes the tools and methods used in this collection project. In crosslanguage retrieval tasks, Tejada attempted to use this self-constructed corpus to improve the quality of query translation for better retrieval performance [20]. The sporting performance of an autonomous underwater vehicle (AUV) is critical to the safety and measurement accuracy of the AUV. However, the relationship between the metrics used to assess athletic performance is often complex and cannot be expressed in mathematical formulas, and it is difficult to figure out how each metric affects the athletic performance of the AUV. Liu proposed a fuzzy comprehensive evaluation (FCE) method to evaluate the motion performance of underwater robots. For the landing underwater vehicle, the process of the FCE method is described in detail, and the FCE system is constructed. In the FCE system, a three-level evaluation index system was established based on the motion characteristics of the landing AUV [21]. Based on the analysis of the investigation process and measurement requirements of the landing underwater vehicle, the fuzzy analytic hierarchy process is used to determine the weight set of each factor set. A one-factor evaluation matrix is obtained by solving a membership function having a ridge distribution. The decision is made by comparing the results of the athletic performance evaluation of the two layout schemes. Field tests show that the evaluation results can objectively and comprehensively reflect the motion performance of the landing underwater robot [22]. The comprehensive strength of a company’s patents is a key factor in measuring its core competitiveness. It is of strategic significance to conduct objective analysis and evaluation of the comprehensive patent strength of enterprises. On the basis of domestic and foreign literature research, case studies, and expert consultation, according to the four aspects of patent creation, application, protection, and management, it initially constitutes the overall evaluation index system of enterprise patent comprehensive strength. It consists of 16 indicators. Through fuzzy comprehensive evaluation and case analysis, it analyzes the evaluation model and method of enterprise patent comprehensive strength. Xu and Song believed that the assessment is essentially multiobjective, multifactor, multilayered, vague, and comprehensive, emphasizing that the evaluation of the indicator system and the rational selection of assessment methods are essential for the comprehensive evaluation of corporate patents. Systematic, comprehensive, and scientific effectively enhance the core competitiveness of enterprises and achieve sustainable development of enterprises [23]. In the field of second language acquisition, the mother tongue has both positive and negative transfer. Appropriate foreign language learning strategies can effectively avoid the negative transfer of mother tongue and improve learners’ oral English ability. Based on the theory of language transfer, Sadeghi focused on error analysis and interlanguage theory. In recent years, in the field of college English teaching, more and more scholars have studied the migration of mother tongue, and the main points are different. However, little research has been done on the impact of mother-tongue transfer on the oral English of non-English majors. This case study selected non-English majors as subjects and analyzed the reasons for oral English errors and language categories. The motivation for the student to learn spoken English is to participate in the graduate oral exam and TOEFL. The subject was asked to take two oral and one interview. Through the analysis and research of experimental data, Sadeghi found that students’ oral English errors are mainly affected by the negative transfer of negative mother tongue and negative cultural migration. The errors in spoken language are mainly reflected in speech, vocabulary, grammar, and pragmatics. Sadeghi will focus on three areas: vocabulary, grammar, and pragmatics. The results also show that the most difficult problem for non-English majors in the English oral process is the lack of vocabulary. It is therefore difficult to express their ideas in English [24].


## 2.2. Introduction to The Corpus

A corpus is actually a collection of linguistic materials that are collected in electronic form and stored in electronic form. These materials are mainly derived from naturally collected corpora. At present, there are many large corpora in China for us to study and analyze, but the larger the corpus may have more corpus sources, the weaker the relevance of some research. For the above reasons and research purposes, we have built a small corpus. In this paper, we mainly study label language in spoken English; so, we need a lot of first-hand English spoken language learners to quantify the grammar of the tagged words. Therefore, the biased corpus of spoken English in Chinese students in the “HSK” dynamic spoken English corpus was also selected as the data analysis.HSK is a standardized international Chinese proficiency test established to test the Chinese proficiency of nonnative Chinese speakers. It evaluates the Chinese proficiency of the test subjects. A small corpus was established with the first-hand corpus of the collected spoken English learners for follow-up, used in analytical studies.CLEC: (Chinese Learner English Corpus) contains an English database of about 1 million English words collected from English-speaking learners. It has a layered English language proficiency, covering high school, junior high school, Elementary school, and Chinese university students who are not English majors and English majors. The material in the corpus is not sufficient to compare between different levels, but it is still a reasonable resource for observing the learning of English-speaking learners in Chinese EFL.LOB: the LOB corpus is a British English version of the English corpus. It contains 500 texts in spoken English, 2000 words, distributed in 15 text categories, 9 of which are informative and 6 imaginative. There are two versions of the LOB corpus: the original version and the version with the POS tag.


## 2.3. Introduction to the Fuzzy Evaluation Method

When evaluating objective things, no matter how precise the words are, the evaluation results are still far from the things themselves. Just as two people see the same thing is beautiful, the degree of beauty of the objects they evaluate is obviously different. In fact, this is because of the characteristics of the language itself. China’s philosophers have long discovered that although the language tool is conducive to communication, it is inherently ambiguous, but the pursuit of things is really the driving force of human beings and the pursuit of true knowledge of things. It is said that beauty is ugly. There is no ugly beauty. It is obvious that this dialectical unity can only open up new ideas for understanding the world, but it is not conducive to more accurate evaluation of things. Buddhism says that everyone has the ability to know the true colors of the world, but the inner heart is covered by some dust in front of them. For a more accurate evaluation of things, it is advocated to feel with the mind. Obviously, the Eastern world has not proposed a more quantitative method of evaluating things. However, the rational thinking of Westerners thinks of using mathematical methods to define some fuzzy evaluations, which is also the reason for fuzzy mathematics.Use fuzzy mathematics to solve some fuzzy phenomena. It is obviously a big innovation to use the accuracy of mathematics to deal with the ambiguity of the world itself. Using this idea, the use of fuzzy mathematics has now expanded from dealing with fuzzy phenomena to more complex and broad disciplines, just as this paper wants to use fuzzy mathematics to solve some of the risk problems in business operations. Fuzzy evaluation deals with the fuzzy evaluation objects by precise digital means and can make a more scientific, reasonable, and practical quantitative evaluation of the data with ambiguity in the information. The evaluation result is a vector, not a point value, and the information contained Rich, and it can not only describe the object being evaluated more accurately but also can be further processed to obtain reference information.


## 2.4. Evaluation Procedure Based on the Fuzzy Evaluation Method

The use of the fuzzy evaluation method for the study of label language in spoken English includes four main steps: one is to determine the set of factors, and the abovementioned indicators through the test are used as factors to build the set in order; the second is to give a set of comments, according to each indicator according to its own. The characteristics and feasible range are set to the evaluation level, and each level has a specific description. The third is to evaluate the degree of each factor, establish a fuzzy relationship matrix, and calculate the weight of the index according to the matrix score; the fourth is to determine the degree of prominent influence. VectorΛ is a computational judgment matrix and nonlinear fuzzy comprehensive evaluation.


### 2.4.1. Determine the Factor Set

All evaluation indicators are numbered sequentially, and a set of indicators for evaluation is set up, denoted byU.
(1)U=u1,u2u3,⋯,un.U=u1,u2u3,⋯,un is the evaluation index, which describes the nature of the evaluation object from different angles, and has different degrees of ambiguity.
(1)
Give a comment set to establish an evaluation level for each indicator according to its scope. Each level of the indicator has its own comment description, and all the indicator level reviews are set up with a comment set, indicated byV(2)V=v1,v2,v3,⋯,vm(2)
The fuzzy relationship matrix and weight vector are determined by the single factor evaluation methodThe individual indicators are evaluated, and the weights of each indicator are calculated by mathematical methods (the analytic hierarchy process is used in this paper) to establish a fuzzy model, that is, the evaluation resultVi of the single factor Ui, and the weight vector R can be expressed as
(3)Ri=ri1,ri2,ri3,⋯,rij.Analytic hierarchy process takes the research object as a system and makes decisions according to the thinking method of decomposition, comparison and judgment, and synthesis. The degree of influence of each factor on the results is quantified, very clear, and clear. This method is especially useful for systematic evaluations of unstructured properties and multiobjective, multicriteria, multiperiod, etc. systematic evaluations.(3)
Determine the degree of influence of the indicator vectorΛ and calculate the final evaluation result. Evaluate the degree of influence of each indicator on performance. The greater the degree of influence, the larger λi, usually taking integers. This paper adopts the “five-scale” method, and the value is 1 ~ 5; if the evaluation index does not have a prominent influence, λi=1, and let λ=max (λ1, λ2, λn), the nonlinear fuzzy matrix synthesis operator can be defined as(4)fa1,a2,⋯,an;x1,x2,⋯,xn;Λ=a1x1λ1+a2x2λ2+⋯+anxnλn12.AssumeA=a1,a2,⋯an, ai>0, and ∑ai=1,(5)X=x1,x2,⋯xn,xi≥1.The vector operator that highlights the degree of influence is represented byΛ=λ1,λ2,⋯λn,xi≥1, where λi≥1.


### 2.4.2. Fuzzy Comprehensive Evaluation

According to the comprehensive evaluation result and the principle of maximum membership degree, the maximum membership principle is one of the basic principles of fuzzy mathematics. It is a direct method for model identification using fuzzy set theory. Forn actual models, it can be expressed as n fuzzy subsets A1 and A2 on the universe X, ..., An, and x0∈X is a specific identification object, if there is i≤n, and Aix=maxA, then x is said to belong to A relatively, which is the principle of maximum membership. The elements corresponding to the final evaluation result and the maximum evaluation index in the evaluation set are taken as the final evaluation result, that is, bj=maxb1,b2,⋯bn.


## 2.4.1. Determine the Factor Set

All evaluation indicators are numbered sequentially, and a set of indicators for evaluation is set up, denoted byU.
(1)U=u1,u2u3,⋯,un.U=u1,u2u3,⋯,un is the evaluation index, which describes the nature of the evaluation object from different angles, and has different degrees of ambiguity.
(1)
Give a comment set to establish an evaluation level for each indicator according to its scope. Each level of the indicator has its own comment description, and all the indicator level reviews are set up with a comment set, indicated byV(2)V=v1,v2,v3,⋯,vm(2)
The fuzzy relationship matrix and weight vector are determined by the single factor evaluation methodThe individual indicators are evaluated, and the weights of each indicator are calculated by mathematical methods (the analytic hierarchy process is used in this paper) to establish a fuzzy model, that is, the evaluation resultVi of the single factor Ui, and the weight vector R can be expressed as
(3)Ri=ri1,ri2,ri3,⋯,rij.Analytic hierarchy process takes the research object as a system and makes decisions according to the thinking method of decomposition, comparison and judgment, and synthesis. The degree of influence of each factor on the results is quantified, very clear, and clear. This method is especially useful for systematic evaluations of unstructured properties and multiobjective, multicriteria, multiperiod, etc. systematic evaluations.(3)
Determine the degree of influence of the indicator vectorΛ and calculate the final evaluation result. Evaluate the degree of influence of each indicator on performance. The greater the degree of influence, the larger λi, usually taking integers. This paper adopts the “five-scale” method, and the value is 1 ~ 5; if the evaluation index does not have a prominent influence, λi=1, and let λ=max (λ1, λ2, λn), the nonlinear fuzzy matrix synthesis operator can be defined as(4)fa1,a2,⋯,an;x1,x2,⋯,xn;Λ=a1x1λ1+a2x2λ2+⋯+anxnλn12.AssumeA=a1,a2,⋯an, ai>0, and ∑ai=1,(5)X=x1,x2,⋯xn,xi≥1.The vector operator that highlights the degree of influence is represented byΛ=λ1,λ2,⋯λn,xi≥1, where λi≥1.


## 2.4.2. Fuzzy Comprehensive Evaluation

According to the comprehensive evaluation result and the principle of maximum membership degree, the maximum membership principle is one of the basic principles of fuzzy mathematics. It is a direct method for model identification using fuzzy set theory. Forn actual models, it can be expressed as n fuzzy subsets A1 and A2 on the universe X, ..., An, and x0∈X is a specific identification object, if there is i≤n, and Aix=maxA, then x is said to belong to A relatively, which is the principle of maximum membership. The elements corresponding to the final evaluation result and the maximum evaluation index in the evaluation set are taken as the final evaluation result, that is, bj=maxb1,b2,⋯bn.


## 3. Experiments

### 3.1. Experimental Steps

The corpus used in this study is the spoken language corpus in CLEC (Chinese Learner English Corpus). The total storage capacity of CLEC is about 100 million words. Among them, the linguistic corpus contains about 87,278,205 words, and the spoken corpus contains about 10,341,729 words. The corpus has a wide range of sources, meaning to represent English at all levels and in all fields in the second half of the 20th century. The method used to extract the corpus in this study is the fuzzy evaluation method. The biggest advantage of this method is that it can separate the pen and mouth corpus in CLEC and extract the required corpus from them, respectively. Unless otherwise stated, the CLEC mentioned refers to its spoken language corpus.The specific steps of this study are as follows:(1)
Collect various fuzzy label expressions by summarizing and arranging the research income of previous researchers and questionnaires(2)
The collected fuzzy label words are searched in the CLEC by the fuzzy evaluation method, and the frequency of each expression in the corpus is recorded(3)
Quantitative analysis based on the obtained data(4)
Extract the concordance of each expression and conduct a qualitative analysis


### 3.2. Data Collection

Through step 1, we found 106 fuzzy label expressions. Through step 2, it was found that the 106 expressions appeared as many as 11072 times in the BNC, and the standard frequency (the frequency of occurrence of such expression per million words) was 1055 times. Of the 106 expressions retrieved, 38 standard frequencies were 0; 35 were between 1-4 (including 1, 4), and 33 were over 5 (including 5).The above data indicates that the use of fuzzy labeling is extremely frequent in the spoken language of native speakers. Some expressions, such asand things of this kind, appear only 1-2 times in CLEC, which can be regarded as accidental; some expressions appear very frequently, such as something like that appears 507 times and or something more. It has appeared as many as 1280 times.


### 3.3. Experimental Environment

The reason why the research objects in this paper are called fuzzy label words is mainly because they cannot appear in the words alone but need to be attached to some words to express a vague concept or category, as shown in the following example:(1)
I will get her some chocolate biscuits or somethingA large amount of corpus extracted from CLEC indicates that fuzzy label words can also be attached to adjective phrases, verb phrases, adverb phrases, prepositional phrases, or clauses, such as the following:(2)
Are you going to say hunters will be prosecuted (VP) or something?(3)
Erm, you see, we were given this test, right, and it was on gravity (Prep.P) or something(4)
And then there is all this, yeah green (Adj.P) or something at the back(5)
No, I meant one of the film actresses that has it done that way (Adv.P) or something(6)
There was nobody more glad than him really but I suppose he thought I would go into debt (embedded sentence) or something, but I would not do thatThe study found that of the 106 fuzzy label words, 95 used the parallel conjunction. Of the 11 unrecognized conjunctions, 9 can be preceded by a parallel conjunction. As in CLEC,things like that and both things like that exist at the same time and are used in large quantities. It can be said that in these nine expressions, juxtaposed conjunctions exist in a recessive state. The two exceptions are etc and blah blah blah. These two expressions are different in internal structure from other labels. However, considering that they also have the structural model of “example + label language,” they also have some obvious pragmatic functions as other label words. This paper also includes them in the category of fuzzy label language.


## 3.1. Experimental Steps

The corpus used in this study is the spoken language corpus in CLEC (Chinese Learner English Corpus). The total storage capacity of CLEC is about 100 million words. Among them, the linguistic corpus contains about 87,278,205 words, and the spoken corpus contains about 10,341,729 words. The corpus has a wide range of sources, meaning to represent English at all levels and in all fields in the second half of the 20th century. The method used to extract the corpus in this study is the fuzzy evaluation method. The biggest advantage of this method is that it can separate the pen and mouth corpus in CLEC and extract the required corpus from them, respectively. Unless otherwise stated, the CLEC mentioned refers to its spoken language corpus.The specific steps of this study are as follows:(1)
Collect various fuzzy label expressions by summarizing and arranging the research income of previous researchers and questionnaires(2)
The collected fuzzy label words are searched in the CLEC by the fuzzy evaluation method, and the frequency of each expression in the corpus is recorded(3)
Quantitative analysis based on the obtained data(4)
Extract the concordance of each expression and conduct a qualitative analysis


## 3.2. Data Collection

Through step 1, we found 106 fuzzy label expressions. Through step 2, it was found that the 106 expressions appeared as many as 11072 times in the BNC, and the standard frequency (the frequency of occurrence of such expression per million words) was 1055 times. Of the 106 expressions retrieved, 38 standard frequencies were 0; 35 were between 1-4 (including 1, 4), and 33 were over 5 (including 5).The above data indicates that the use of fuzzy labeling is extremely frequent in the spoken language of native speakers. Some expressions, such asand things of this kind, appear only 1-2 times in CLEC, which can be regarded as accidental; some expressions appear very frequently, such as something like that appears 507 times and or something more. It has appeared as many as 1280 times.


## 3.3. Experimental Environment

The reason why the research objects in this paper are called fuzzy label words is mainly because they cannot appear in the words alone but need to be attached to some words to express a vague concept or category, as shown in the following example:(1)
I will get her some chocolate biscuits or somethingA large amount of corpus extracted from CLEC indicates that fuzzy label words can also be attached to adjective phrases, verb phrases, adverb phrases, prepositional phrases, or clauses, such as the following:(2)
Are you going to say hunters will be prosecuted (VP) or something?(3)
Erm, you see, we were given this test, right, and it was on gravity (Prep.P) or something(4)
And then there is all this, yeah green (Adj.P) or something at the back(5)
No, I meant one of the film actresses that has it done that way (Adv.P) or something(6)
There was nobody more glad than him really but I suppose he thought I would go into debt (embedded sentence) or something, but I would not do thatThe study found that of the 106 fuzzy label words, 95 used the parallel conjunction. Of the 11 unrecognized conjunctions, 9 can be preceded by a parallel conjunction. As in CLEC,things like that and both things like that exist at the same time and are used in large quantities. It can be said that in these nine expressions, juxtaposed conjunctions exist in a recessive state. The two exceptions are etc and blah blah blah. These two expressions are different in internal structure from other labels. However, considering that they also have the structural model of “example + label language,” they also have some obvious pragmatic functions as other label words. This paper also includes them in the category of fuzzy label language.


## 4. Discussion

### 4.1. Studying the Results of Labeling Experiments in Spoken English

This paper uses CLEC (Chinese English Learner Corpus) to systematically study the use of Chinese students in this type of expression and found the following characteristics: first, the total use is too low, and the individual use is excessive. The 106 tagged words found in CLEC were searched one by one in COLSEC. CLEC collected more than one million words in five types of students, including middle school students, college English grades 4 and 6, and professional English grades and senior grades, and marked speech errors. Its purpose is to observe the English characteristics and language errors of various students, hoping to make a more accurate description of Chinese learners’ English through quantitative and qualitative methods and to provide useful feedback information for Chinese students’ English teaching. It was found that only 19 expressions appeared in the corpus, with a total frequency of only 271 and a standard frequency of 371. The total frequency of this type of expression in LOB is 11072 times. The standard frequency is 1055 times, as shown in Figure1.Figure 1
The occurrence rate of tagged words in CLEC and LOB.In order to better compare the difference in the frequency of use of these two groups of expressions, this paper uses the fuzzy evaluation test formulax2=∑O−E2/E to calculate the two sets of data, and the card value is 443.47. This value shows that the difference between the two is extremely significant. Tables 1 and 2 list the frequency of 10 fuzzy tag expressions appearing in COLSEC and LOB and their chi-squared information. Chi-square is a statistic in nonparametric tests, mainly to compare the correlation analysis of two or more sample rates. Its fundamental idea is to compare the degree of agreement or goodness of fit between the theoretical frequency and the actual frequency.Table 1
Comparison of the frequency and chi-square value of fuzzy label language in LOB.
Vagueness tagsLOBx2RFNFAnd so forth118119.09And something3642.25And the rest810.00And that7557371.01Or anything5165048.02Anything like that2682624.04Or so1201210.08Something like that7987771.12Or what38414.00Or something like that4013729.43Table 2
Comparison of the frequency and chi-square value of fuzzy label language in CLEC.
Vagueness tagsCLECx2RFNFAnd so forth119.09And something112.25And the rest110.00And that1171.01Or anything1148.02Anything like that1124.04Or so1110.08Something like that2271.12Or what234.00Or something like that4529.43Further observation of Tables1 and 2 reveals that most of the 10 expressions present in CLEC are used at low frequencies, 7 of which have only occurred once. The standard frequency of these labels in two corpora is calculated by fuzzy evaluation test. It is found that except for and something and the rest, the other expressions are extremely significant. Among them, Chinese students use more than a few expressions of and soon, or something else, and something else, and or things like that, especially for the excessive use of the first two expressions. At the same time, the use of other words is significantly less. The specific performance is shown in Figures 2 and 3.Figure 2
Comparison of labeling rate and standard rate in CLEC.Figure 3
Comparison of labeling rate and standard rate in LOB.These data show that, first of all, Chinese English learners have significant differences in the total use of this type of words compared with native speakers, indicating that the overall attention to this type of words is not high. Second, they do not know much about the types of fuzzy expressions. This is also one of the manifestations of its vocabulary. Chinese English learners not only have quantitative differences in the use of fuzzy label language compared with native speakers but also have significant differences in function, mainly manifested as functional unity. The most common function of Chinese students using such expressions is that the table information is omitted. In addition, since the position of such expression is generally located after an utterance, it can also objectively perform the role of turn-taking. As for the other pragmatic functions used by native speakers, such as language strengthening, courtesy, or identity, they are not reflected in CLEC. The corpus extracted from the LOB shows that although many fuzzy tag words are structurally similar, they are subject to certain constraints in most cases due to specific context requirements. However, Chinese students do not know enough about the constraints on these expressions in the context, which often leads to improper use.


### 4.2. Analysis of English Spoken Collocation Errors

Although the fuzzy evaluation analysis shows that the learner’s learning of the collocation knowledge is not satisfactory, if you do not know which collocation type or which component of the collocation poses the greatest difficulty for the learner, you will never know how to improve the collocation teaching; Meaning, in order to solve the specific problems encountered by Chinese English learners in collocation selection, a two-dimensional error classification method is adopted in the error description, which classifies the errors of each collocation pattern into several categories according to their characteristics. The results of the descriptive analysis reveal that the speaker will produce a deviation combination when trying to find a match for a given node, because he ignores or does not have enough knowledge about the semantic properties, collocation constraints, or semantic prosody of the two words. And the deviation not only occurs in the different elements of the collocation but also in the overall collocation. For learners, noun-nouns and verb-nouns pose the greatest difficulty, and for each collocation form, the most common occurrence is the collocation. The anomalous usage of the six types of collocations is summarized below. Verb noun (C1), adjective noun (C2), noun-verb collocation error (C3), noun (including noun-noun) collocation (C4), adverb-adjective collocation error (C5), and verb-adverb collocation error (C6). In summary, Chinese English learners have encountered the greatest difficulty in using noun-noun and verb-noun patterns when speaking, while verb-adverbs are the best way to learn and use. As shown in Figure4, the difficulty level that the learner encounters when making the collocation selection will decrease from left to right along the level, while the combination of nouns and verb adverbs is located at both ends of the line.Figure 4
English speaking collocation difficulty.


## 4.1. Studying the Results of Labeling Experiments in Spoken English

This paper uses CLEC (Chinese English Learner Corpus) to systematically study the use of Chinese students in this type of expression and found the following characteristics: first, the total use is too low, and the individual use is excessive. The 106 tagged words found in CLEC were searched one by one in COLSEC. CLEC collected more than one million words in five types of students, including middle school students, college English grades 4 and 6, and professional English grades and senior grades, and marked speech errors. Its purpose is to observe the English characteristics and language errors of various students, hoping to make a more accurate description of Chinese learners’ English through quantitative and qualitative methods and to provide useful feedback information for Chinese students’ English teaching. It was found that only 19 expressions appeared in the corpus, with a total frequency of only 271 and a standard frequency of 371. The total frequency of this type of expression in LOB is 11072 times. The standard frequency is 1055 times, as shown in Figure1.Figure 1
The occurrence rate of tagged words in CLEC and LOB.In order to better compare the difference in the frequency of use of these two groups of expressions, this paper uses the fuzzy evaluation test formulax2=∑O−E2/E to calculate the two sets of data, and the card value is 443.47. This value shows that the difference between the two is extremely significant. Tables 1 and 2 list the frequency of 10 fuzzy tag expressions appearing in COLSEC and LOB and their chi-squared information. Chi-square is a statistic in nonparametric tests, mainly to compare the correlation analysis of two or more sample rates. Its fundamental idea is to compare the degree of agreement or goodness of fit between the theoretical frequency and the actual frequency.Table 1
Comparison of the frequency and chi-square value of fuzzy label language in LOB.
Vagueness tagsLOBx2RFNFAnd so forth118119.09And something3642.25And the rest810.00And that7557371.01Or anything5165048.02Anything like that2682624.04Or so1201210.08Something like that7987771.12Or what38414.00Or something like that4013729.43Table 2
Comparison of the frequency and chi-square value of fuzzy label language in CLEC.
Vagueness tagsCLECx2RFNFAnd so forth119.09And something112.25And the rest110.00And that1171.01Or anything1148.02Anything like that1124.04Or so1110.08Something like that2271.12Or what234.00Or something like that4529.43Further observation of Tables1 and 2 reveals that most of the 10 expressions present in CLEC are used at low frequencies, 7 of which have only occurred once. The standard frequency of these labels in two corpora is calculated by fuzzy evaluation test. It is found that except for and something and the rest, the other expressions are extremely significant. Among them, Chinese students use more than a few expressions of and soon, or something else, and something else, and or things like that, especially for the excessive use of the first two expressions. At the same time, the use of other words is significantly less. The specific performance is shown in Figures 2 and 3.Figure 2
Comparison of labeling rate and standard rate in CLEC.Figure 3
Comparison of labeling rate and standard rate in LOB.These data show that, first of all, Chinese English learners have significant differences in the total use of this type of words compared with native speakers, indicating that the overall attention to this type of words is not high. Second, they do not know much about the types of fuzzy expressions. This is also one of the manifestations of its vocabulary. Chinese English learners not only have quantitative differences in the use of fuzzy label language compared with native speakers but also have significant differences in function, mainly manifested as functional unity. The most common function of Chinese students using such expressions is that the table information is omitted. In addition, since the position of such expression is generally located after an utterance, it can also objectively perform the role of turn-taking. As for the other pragmatic functions used by native speakers, such as language strengthening, courtesy, or identity, they are not reflected in CLEC. The corpus extracted from the LOB shows that although many fuzzy tag words are structurally similar, they are subject to certain constraints in most cases due to specific context requirements. However, Chinese students do not know enough about the constraints on these expressions in the context, which often leads to improper use.


## 4.2. Analysis of English Spoken Collocation Errors

Although the fuzzy evaluation analysis shows that the learner’s learning of the collocation knowledge is not satisfactory, if you do not know which collocation type or which component of the collocation poses the greatest difficulty for the learner, you will never know how to improve the collocation teaching; Meaning, in order to solve the specific problems encountered by Chinese English learners in collocation selection, a two-dimensional error classification method is adopted in the error description, which classifies the errors of each collocation pattern into several categories according to their characteristics. The results of the descriptive analysis reveal that the speaker will produce a deviation combination when trying to find a match for a given node, because he ignores or does not have enough knowledge about the semantic properties, collocation constraints, or semantic prosody of the two words. And the deviation not only occurs in the different elements of the collocation but also in the overall collocation. For learners, noun-nouns and verb-nouns pose the greatest difficulty, and for each collocation form, the most common occurrence is the collocation. The anomalous usage of the six types of collocations is summarized below. Verb noun (C1), adjective noun (C2), noun-verb collocation error (C3), noun (including noun-noun) collocation (C4), adverb-adjective collocation error (C5), and verb-adverb collocation error (C6). In summary, Chinese English learners have encountered the greatest difficulty in using noun-noun and verb-noun patterns when speaking, while verb-adverbs are the best way to learn and use. As shown in Figure4, the difficulty level that the learner encounters when making the collocation selection will decrease from left to right along the level, while the combination of nouns and verb adverbs is located at both ends of the line.Figure 4
English speaking collocation difficulty.


## 5. Conclusions

The fuzzy label language is used frequently in the spoken language of native speakers and has many functions. It is a very important expression in English. Chinese English learners still have a poor grasp of such expressions. Regardless of the diversity of expression, the frequency of use, or the function of use, the gap between the native speakers is relatively large. The root causes are the following: first, it is accurate and light. Learners generally only pay attention to the accuracy of language, and the correct grammar and accurate words are the ultimate goal. The vague language is considered to be a poorly expressed language. It is not known that in some cases, the accuracy of the language can only be achieved through ambiguous words. Moreover, the use of ambiguous words can have unexpected effects. Second, there are heavy written language and light spoken. Traditional English learning materials are generally relatively orthodox, formal language models, that is, written language. In recent years, the communicative function of language has received a certain degree of attention, and the importance of listening and speaking has been highlighted. However, most of the corpus sources in listening and speaking textbooks are not true enough. Most of them are also written words that have been adapted. In addition, students lack practical context to practice the language points they learn, which leads them to speak a lot in spoken English. Program expressions (slang, idioms, slang, and discourse markers) that appear at high frequencies are not well understood. Third, there is a lack of real language input. Since the way most Chinese students learn English is still limited to textbooks, this leads to a poor quality and quantity of students’ language output. When it is necessary to express a certain meaning, for the sake of insurance and convenience, they prefer to use a few options with certainty and rarely try other expressions. Fourth, learn vocabulary in isolation. At present, English teaching is still based on grammar and vocabulary teaching. The vocabulary teaching method still stays in the mode of “interpretation—example-sentence.” The vocabulary knowledge that students master through this model is often isolated and one-sided. The specific meaning of a word can only be fully revealed in a specific context. The semantics of the program expressions mentioned above are not obtained through the semantic combination of individual words, and the textual and pragmatic functions of these expressions can only be embodied in specific contexts.There are many aspects involved in solving the above problems. This paper believes that the most effective way is to promote three-dimensional textbooks and three-dimensional teaching methods, strengthen the real language input, and encourage language output in real context. Many of the problems in the use of Chinese English learners in English are actually related to the lack of real language input or a single. In view of the fact that the channels for Chinese students to learn English are still based on classrooms and textbooks, the most direct way to change this situation is to start with the current teaching materials and teaching methods. The high development of modern information technology has made it possible to develop three-dimensional textbooks and three-dimensional teaching. The three-dimensional textbook requires that the teaching content should be based on the objective law of language learning and the objective use of the native language. The selection should be based on the level and needs of the learning group. The real corpus is the basis, not subject to the editor’s personal sense of language, knowledge structure, or hobbies; the diversification and three-dimensionality should be reflected in the arrangement. In addition to the traditional paper text materials, the corresponding audio and video materials should be included when necessary to increase the authenticity and vividness of the textbook.The effective implementation of the three-dimensional textbook depends on the three-dimensional teaching method. This requires teachers to be diversified and three-dimensional in the classroom. The teacher’s task is no longer to instill “right use” into the students, but to “show” the “objective use” of the native speakers in the real context. After receiving these “objective uses,” teachers should create certain real or near-real contexts for students in and out of the classroom and encourage them to actively export their language. Only in this way can students truly internalize the knowledge they have learned, and only in this way can they effectively narrow the gap between English learners and native speakers in language use and eliminate communication barriers between them.



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*Source: 2511306-2022-03-12.xml*