基于语料库的中国理工科大学生英语口语连接词使

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ABSTRACT

English connectives are one of the important cohesive devices to realize discourse

cohesion and coherence at the syntactic and logical-semantic levels and they play

important roles in language communication. A great deal of research home and abroad

was conducted on the use of connectives in English as a foreign language (EFL)

learners’ writing and has revealed learners’ problems of using connectives such as

overuse, underuse or erroneous use. However, very few studies were based on learners’

oral English. Meanwhile, corpus linguistics provides multi-dimensional insights into

the features of EFL learners’ interlanguage. Thus, the present study employs corpus

linguistic theories, research methods such as Contrastive Interlanguage Analysis (CIA)

and corpus analytical tools to carry out a systematic study on Chinese Science and

Engineering Majors’ (SEMs’) use of English connectives in oral English. It is designed

to unfold the features of using connectives by SEMs across different English

proficiency levels (i.e. high-level, middle-level and low-level) as well as the features

and problems in comparison with native speakers’ norms. SEMs’ oral materials are

collected and divided into the corresponding three sub-databases according to students’

English proficiency levels. Native speakers’ database is mainly extracted from the

large-scale Corpus of Contemporary American English (COCA). Based on the

quantitative and qualitative analysis, this thesis obtains the following findings.

(1) There is no statistically significant difference in the overall frequency of using

connectives among SEMs’ levels but a significant difference exists in the use of

connective varieties, which increases with the rise of SEMs’ English proficiency. There

is no statistically significant difference on the use of most semantic categories across

SEMs’ levels except in the inferential semantic category. The comparison of top-ten

connectives (TTCs) shows that there are many similarities in the choice of TTCs, i.e.

seven out of ten connectives are identical. The frequency of TTCs shows that there is

no statistically significant difference on the use of most TTCs. The analysis of the

context shows that all levels of SEMs have many problems in using connectives at the

syntactic and logical-semantic levels. The statistics on the erroneous use shows that

there is a significant difference in redundancy of connectives use across levels but no

significant difference in incorrect choice of connectives. In general, the feature of

using connectives in oral English by SEMs across different English proficiency levels

shows more similarities than differences.

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(2) Compared with native speakers, all levels of SEMs tend to overuse English

connectives but lack of varieties. The comparison on the use of semantic categories

shows that all SEMs tend to overuse additive, enumerative, causal, resultive and

contrastive semantic categories, but underuse the transitional relation. The use of TTCs

shows that all SEMs perform similarly with native speakers in the choice of TTCs and

the degree of similarity increases with the rise of SEMs’ English proficiency. In the

frequency of TTCs, SEMs across English levels perform differently from native

speakers. In general, the extent of overusing connectives is much stronger than that of

underusing. All SEMs have a heavy reliance on the use of a limited variety of

connectives.

(3) As for the problems of using English connectives by SEMs in their oral

English, this thesis analyzes the underlying causes from the aspects of first language

(L1) transfer and insufficient knowledge about target language. Some implications for

English learning and teaching are then put forward and expected to help students better

internalize the native speakers’ norms of oral expression and improve their accuracy of

using connectives in oral communication.

Due to various constraints, this thesis has several limitations, which requires

further discussion and research.

Key words: connectives, Chinese SEMs, corpus, CIA

摘要

连接词是语篇在句法和语义逻辑层面上实现连贯的重要衔接手段之一，对语

言交际产生重要影响。国内外很多学者针对英语学习者写作中使用连接词的情况

进行了研究，发现英语学习者有多用、少用甚至错误使用连接词的问题。然而，

对英语学习者口语中连接词使用情况的研究却很少。同时，语料库语言学为研究

英语学习者的语言使用特点提供了多维视角和方法。鉴于此，本文运用语料库语

言学的相关理论，研究方法(如中介语对比分析)和语料库分析工具对中国理工科

大学生口语中使用英语连接词的情况进行了研究,主要目的是探讨他们在口语中

使用连接词的特征以及有别于英语本族语者使用的异同点和问题，从而为英语口

语教学提供帮助。本研究所用的理工科学生口语语料根据学生的英语水平（即高、

中、低）分为相应的三个子数据库。参照语料主要采自当代美语语料库（COCA）

中的口语数据库。通过定量和定性分析，笔者发现：

（1）高、中、低三个水平组在使用连接词的总频数上没有显著差异，但在

种类使用的丰富度上存在着显著差异，即种类的丰富度随着英语水平的增高而增

加。语义分布上除了在推断语义使用上有显著差异外，三个水平组在其它语义的

使用上均没有显著差异。各数据库中的前 10 个连接词的比较显示三个水平组在

选词上很相似，10 个中有 7 个相同，并且在多数词的使用频数上没有显著差异。

三个水平组在逻辑语义和句法层面上均出现了误用连接词的问题，错误率统计显

示三个水平组学生在误选连接词上没有显著差异，但在冗余问题上存在着显著差

异。总体来看，三个水平组口语中使用连接词的相同点多于不同点。

（2）与本族语者相比，理工科三个水平组学生使用连接词的频数过多而使

用连接词的种类偏少。语义分布显示理工科大学生都倾向于过多使用增补、列举、

原因、结果和对比关系的语义，而过少使用过渡关系的语义。各数据库中的前 10

个连接词的比较显示理工科大学生与本族语者在选词的倾向上很相似，并且这种

相似度随着英语水平的增高而增大，但是在其使用频数上各水平组学生与本族语

者有较大差异。总体来看，学习者过多使用连接词的程度要高于过少使用，并且

在种类的使用上过分依赖于少数的几个连接词。

（3）针对理工科各水平组学生在口语中都出现的错误使用、多用和少用连

接词的问题，本文从母语迁移和知识掌握等方面进行了分析。对学生的英语学习

和教师的英语教学提出了一些启示，以期帮助学生更好地内化地道的英语口语表

达，提高他们口语中使用连接词的能力。

鉴于各种原因，此项研究仍有许多不完善之处，需要进一步探讨和研究。

关键词：连接词中国理工科大学生语料库中介语对比分析

List of Acronyms

ANOVA

Analysis of Variance

Contrastive Analysis

CET

College English Test

CET4

College English Test Band 4

CET6

College English Test Band 6

CIA

Contrastive Interlanguage Analysis

CLC

Cambridge Learner Corpus

CLEC

Chinese Learners English Corpus

COCA

Corpus of Contemporary American English

COLSEC

College Learners’ Spoken English Corpus

CQUPT

Chongqing University of Posts and Telecommunications

DDL

Data-Driven-Learning

ECUST

East China University of Science and Technology

EFL

English as a Foreign Language

ESL

English as a Second Language

HIT

Harbin Institute of Technology

ICLE

International Corpus for English Learners

LINDSEI

Louvian International Database of Spoken English Interlanguage

LLC

Longman Learners’ Corpus

First Language

LSWEC

Longman Spoken and Written English Corpus

NUST

Nanjing University of Science and Technology

SCCNN

Spoken Corpus of Cable National News

SEMs

Science and Engineering Majors

SLA

Second Language Acquisition

SPSS

Statistical Package for Social Science

SSTC

Standard Speaking Test Corpus

SWECCL

Spoken and Written English Corpus of Chinese Learners

TTCs

Top-ten Connectives

USST

University of Shanghai for Science and Technology

List of Tables

Table 4.1 Connectives investigated in this study……………………………………...27

Table 4.2 Description of universities in SEMs’ databases…………………………….28

Table 4.3 Description of spoken databases used in this study………………………...32

Table 5.1 Overall frequencies and varieties in SEMs’ sub-databases…………………39

Table 5.2 One-way ANOVA tests on overall frequency and variety within SEMs’

databases……………………………………………………………………40

Table 5.3 Post Hoc test on variety within SEMs’ databases…………………………..41

Table 5.4 Semantic distributions within SEMs’ databases…………………………....42

Table 5.5 One-way ANOVA tests on semantic distribution within SEMs’ databases...43

Table 5.6 Post Hoc test on the inferential category within SEMs’ databases………....44

Table 5.7 TTCs in SEMs’ databases…………………………………………………..45

Table 5.8 One-way ANOVA tests on TTCs within SEMs’ databases………………....46

Table 5.9 Post Hoc tests on too and then (inferential)………………………………...47

Table 5.10 Incorrect choice of connectives within SEMs’ databases………………....50

Table 5.11 One-way ANOVA test on incorrect choice of connectives……………......50

Table 5.12 Redundancy of connectives within SEMs’ databases……………………..53

Table 5.13 One-way ANOVA test on redundancy of connectives...…………………..54

Table 5.14 Post Hoc tests on redundancy of connectives……………………………..54

Table 5.15 Overall frequencies and varieties in SEMs’ databases and SCCNN……...55

Table 5.16 LL tests on overall frequency……………………………………………...56

Table 5.17 Semantic distributions in SEMs’ databases and SCCNN…………………57

Table 5.18 TTCs in SEMs’ databases and SCCNN…………………………………...59

Table 5.19 LL tests on connectives over/underused by the high-level group…………61

Table 5.20 LL tests on connectives over/underused by the middle-level group...….....62

Table 5.21 LL tests on connectives over/underused by the low-level group...………..63

vii

List of Figures

Figure 4.1 Screenshot of AntConc 3.2.1……………………………………................33

Figure 4.2 Screenshot of Log-likelihood and Chi-square Calculator 1.0……………..35

Figure 5.1 Semantic distributions within SEMs’ databases…………………………...42

Figure 5.2 Semantic distributions in SEMs’ databases and SCCNN……………….....58

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摘要：

iiABSTRACTEnglishconnectivesareoneoftheimportantcohesivedevicestorealizediscoursecohesionandcoherenceatthesyntacticandlogical-semanticlevelsandtheyplayimportantrolesinlanguagecommunication.AgreatdealofresearchhomeandabroadwasconductedontheuseofconnectivesinEnglishasaforeignlanguage(EFL)learners’writ...

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