UNDERSTANDING THE EMOTIONS BEHIND SOCIAL IMAGES INFERRING WITH USER
UNDERSTANDING THE EMOTIONS BEHIND SOCIAL IMAGES:INFERRING WITH USER
DEMOGRAPHICS
Boya Wu1,2,3,Jia Jia1,2,3,Yang Yang1,Peijun Zhao1,2,3,Jie Tang1 1Department of Computer Science and Technology,Tsinghua University,Beijing100084,China 2Key Laboratory of Pervasive Computing,Ministry of Education
3Tsinghua National Laboratory for Information Science and Technology(TNList)
stella.1991@https://www.wendangku.net/doc/9a984709.html,,jjia@https://www.wendangku.net/doc/9a984709.html,,sherlockbourne@https://www.wendangku.net/doc/9a984709.html,
421769833@https://www.wendangku.net/doc/9a984709.html,,jietang@https://www.wendangku.net/doc/9a984709.html,
ABSTRACT
Understanding the essential emotions behind social images is of vital importance:it can bene?t many applications such as image retrieval and personalized recommendation.While previous related research mostly focuses on the image vi-sual features,in this paper,we aim to tackle this problem by “linking inferring with users’demographics”.Speci?cally, we propose a partially-labeled factor graph model named D-FGM,to predict the emotions embedded in social images not only by the image visual features,but also by the information of users’demographics.We investigate whether users’demo-graphics like gender,marital status and occupation are related to emotions of social images,and then leverage the uncov-ered patterns into modeling as different factors.Experiments on a data set from the world’s largest image sharing website Flickr1con?rm the accuracy of the proposed model.The ef-fectiveness of the users’demographics factors is also veri?ed by the factor contribution analysis,which reveals some inter-esting behavioral phenomena as well.
Index Terms—Emotion,image,users’demographics
1.INTRODUCTION
Emotion stimulates the mind3,000times faster than ratio-nal thoughts[1].With the rapid development of social net-works,people get used to sharing their emotional experiences on these platforms.As a natural way to express our feelings, images are uploaded and shared on social networks.We de-?ne these images as“Social Images”.Our preliminary statis-tics indicate that38%of the images on the world’s largest image social network Flickr are explicitly annotated with ei-ther positive or negative emotions by their uploaders.Un-derstanding the essential emotions behind social images is of vital signi?cance.It can bene?t many applications,such as image retrieval and personalized recommendation.
When it comes to inferring emotions from social im-ages,previous related research mainly focuses on the image 1http://www.?https://www.wendangku.net/doc/9a984709.html,/visual features,which means enhancing the emotion infer-ring performance by extracting the effective visual features and choosing their proper combinations.J.Machajdik and A.Hanbury[2]investigate four categories of low-level fea-tures like wavelet textures and GLCM-features.S.Zhao[3] et al.explore principles-of-art features for image emotion recognition.Similar works can be found in[4],[5].
Recently,the research on social networks has veri?ed that the users’demographics are associated with the users’be-haviors.Y.Dong et al.[6]discover that people of different ages have different social strategies to maintain their social connections.H.Huang et al.[7]uncover how users’demo-graphics in?uence the formation of closed triads on social net-works.Moreover,the behavioral research has proved that the human perception of emotions varies according to their per-sonal attributes.A.Fischer et al.[8]point out that there is a gender difference in the perception of emotions,namely that men report more powerful emotions(e.g.,anger),whereas women report more powerless emotions(e.g.,sadness,fear). However,can the user’s demographics be leveraged to help infer the emotions from social images is still largely undevel-oped.The problem is non-trivial and has several challenges. First,though a few literatures demonstrate the existence of the correlation between the users’demographics and the per-ception of emotions,it is still unclear whether the correlation exists on image social networks.Second,how to model the users’demographics and other information in a joint frame-work?Third,how to validate the effectiveness of the pro-posed model on a real-world image social network?
To address these challenges,?rst we investigate whether users’demographics like gender,marital status and occupa-tion are related to the emotions of social images.Then we leverage the uncovered patterns into modeling as different factors.Speci?cally,we propose a partially-labeled factor graph model named D-FGM,to infer emotions from social images not only by the visual features,but also by the in-formation of users’demographics.As for experiments,we construct a library of millions of images and users(2,060,353
images and1,255,478users)from the world’s largest image sharing website Flickr.The experimental results con?rm the accuracy of the proposed model,e.g.,achieving19.4%im-provement compared with SVM(Support Vector Machine) under the evaluation of F1-Measure.The effectiveness of the users’demographics factors is also demonstrated by the factor contribution analysis,which reveals some interesting behav-ioral phenomena.For example,in terms of sadness,the image emotion is mainly determined by the visual features.Interest-ingly however,when it comes to disgust and surprise,males and females have different emotion perception;when it comes to fear,whether the user is single or taken makes differences; and when it comes to happiness and anger,the perception of these emotions is associated with the user’s occupation.
2.PROBLEM DEFINITION
In this section,we give several necessary de?nitions and for-malize the problem.
Users’demographics:The users’demographics usually refer to the users’personal attributes,which for example,con-tain the age,gender,location information in[6].In this paper, we present user v i’s demographics as three vectors p i:gen-der,marital status and occupation.The gender is de?ned as male or female.The marital status is de?ned as single or taken.For the occupation,by manually screening the users’pro?les on Flickr,we pick out25main kinds of occupations and classify them into two categories,namely,the artists and the engineers.The“artists”include writer,musician,dancer, etc.and the“engineers”include programmer,scientists,etc.
Image social network:A partially-labeled time-varying image social network can be de?ned as G= (V,P,E t,X L,X U),where V is the set of|V|=n users, P={p i}is the set of the users’demographics,E t?V×V is the friendship among users at time t,X L represents the labeled images and X U represents the unlabeled images.
Emotion:The emotion of user v i at time t is denoted as y t i.The emotion of the image x t i,j uploaded by user v i at time t is denoted as y t i,j,where j is the index of images uploaded by user v i.
In this work,we have the following intuition:users’emo-tions are expressed by the emotions of the images they upload on image social networks,which means y t i=y t i,j.
We adopted Ekman’s[9]classical theory of basic human emotion categories,namely,happiness,surprise,anger,dis-gust,fear and sadness and denote the emotional space as R.
Based on the above de?nitions,the learning task of our model is put forward as follows.
Learning task:Given a partially-labeled time-varying image social network G=(V,P,E t,X L,X U),?nd a func-tion f to predict the emotions from unlabeled images:
f:G=(V,P,E t,X L,X U)→Y(1) where Y={y t i,j}∈R.
3.OBSERV ATIONS
Users’demographics have been veri?ed to be associated with users’behaviors in social networks[6],[7].Wondering whether users’demographics make differences on users’per-ception of emotions,we conduct a series of observations and present several interesting phenomena we have discovered.
3.1.Data collection
We randomly download2,060,353images and1,255,478 users’pro?les from Flickr.To conduct the observations,?rst we need to know the primary emotion of images.Owing to the massive scale of our data set,manually labeling the emotion for every image is not practical.Herein we adopt a method to label the emotion of images automatically.This method is also used by Xie[10]and Hwang[11].First we construct word lists for each of the six emotion categories through WordNet2and HowNet3.Next we compare the im-age tags written by the uploader with every word list and the image can be labeled with a type of emotion whose word list match the words of the tags most frequently.In this way, 218,816images are labeled.These images are uploaded by 2,312users,and each emotional category contains101189, 21169,17491,11571,37791,29605images.
3.2.Observations on users’demographics Herein we observe the correlation between image emotions and the three parts of the users’demographics respectively.
Observation on the gender correlation.First we clas-sify the users into males and females,each containing363 and1,670users.Then we randomly pick out2,000images from each emotion category,half uploaded by males and the other half uploaded by females and analyze the distributions of the visual features of the images.Figure1(a)presents sev-eral representative results.We can see that in the case of dis-gust,the distributions of visual features of images uploaded by males and females are different.For instance,the satura-tion(S)of the images uploaded by females is21.4%lower than the images uploaded by males.It suggests that though both males and females want to express their disgust through images,they tend to use different visual features to convey their feelings.In terms of surprise,the cool color ratio(CCR) of the images uploaded by females is19.9%higher than the images uploaded by males,showing that males and females have different ways to express their surprise.The observation results can be concluded that there is a gender difference in the emotion perception of social images.
Observations on the marital status correlation.Sim-ilarly,according to the user’s marital status,we divide the users into single and taken,each containing310and954 users.We conduct the observations again and the results are visualized in Figure1(b).The distributions of visual features of images uploaded by single users and taken users are dif-ferent in fear and sadness.For example,in terms of sadness, 2https://www.wendangku.net/doc/9a984709.html,/
3https://www.wendangku.net/doc/9a984709.html,/
(a)The difference of the distributions of the visual features of the images uploaded by females and
males.
(b)The difference of the distributions of the visual features of the images uploaded by single users and taken
users.
(c)The difference of the distributions of the visual features of the images uploaded by engineers and artists.
Fig.1.The difference of the distributions of representative visual features of images uploaded by users with different personal attributes,which shows the correlation between image emotions and users’demographics.The features include:S:saturation,SC:saturation contrast,B:brightness DCR:dull color ratio,CCR:cool color ratio,CD:color difference,AD:area difference,TB:texture complexity of background.The values of features are normalized between 0and 1over the whole data set.the saturation (S )of the images uploaded by single users is 15.3%lower than the images uploaded by taken users,and in terms of fear ,the background texture complexity (TB )of the images uploaded by single users is 11.0%higher than the images uploaded by taken users.The results show that single users and taken users use different ways to express the same feeling,indicating that their emotion perception of social im-ages differs.
Observations on the occupation correlation.As de-scribed in the problem de?nition section,we carefully select 217users as “engineers”and 279users as “artists”.We con-duct the observations again and Figure 1(c)illustrates the re-sults.In terms of happiness ,the brightness of the image up-loaded by engineers is 7.6%higher than the images uploaded by artists.In terms of anger ,the cool color ratio of the im-ages uploaded by engineers is (CCR )is 11.8%lower the im-ages uploaded by artists.The results suggest that on image social networks,engineers and artists have different emotion perception.
The observation can be summarized as follows:?Males and Females have different ways to express dis-gust and surprise .There is a gender difference in the emotion perception of social images.
?Single users and taken users use different ways to ex-press fear and sadness ,indicating that their emotion perception for these emotions is different.
?Engineers and artists use different ways to convey hap-piness and anger ,suggesting that the occupation may be related to the users’emotion perception.
4.MODEL
To leverage the above ?ndings to help infer emotions from so-cial images,we propose a factor graph model named D-FGM to solve the problem.Our basic idea is to de?ne the corre-lations using different types of factor functions.In a factor graph model,the objective function is de?ned based on the joint probability of the factor functions [1],[12],so the prob-lem of emotion model learning is cast as the model parameters learning that maximizes the joint probability.
In our model,four types of correlations can be de?ned as factor functions.
?Visual features correlation f 1(u t i,j ,y t
i,j ).It represents the correlation between the visual features u t i,j and the
image emotion y t
i,j .
?Temporal correlation f 2(y t i ,y t
i ).Previous research has veri?ed that there is a strong dependency between one’s current emotion and the emotions in the recent past on social networks [1].This correlation is de?ned as temporal correlation,which represents the in?uence of the user’s previous emotions in the recent past t ’on the current emotion at time t .
?Social correlation .Creating and sharing images on image social networks is very different from traditional creation.Some users may have a strong in?uence on their friends’emotions and some emotions may spread quickly on the social network [5].The social correla-tion contains three parts:the correlation between the image emotion and the number of the user’s friends
f 3(s t i ,y t
i,j ),the correlation between the image emotion
Algorithm 1The learning and inference algorithm of emo-tions from social images.
A partially-labeled time-varying image social network G =(V,P,E t ,X L ,X U )and the learning ratio λOutput:
Construct a partially-labeled factor graph.Initiate parameters θ={α,β,γ,δ,εi ,ηi,j }repeat
Calculate E (p θ(Y |Y U ,G ))S using LBP Calculate E (p (Y |G ))S using LBP
Calculate the gradient of θ:E (p θ(Y |Y U ,G ))S ?E (p θ(Y |G ))S
Update θwith the learning ratio λ:θ=θ0+??
?θλuntil convergence
Get the inference results Y =y t i,j ,y t
i,j ∈R and the trained parameters θ={α,β,γ,δ,εi ,ηi,j }
and the major emotion of the user’s friends f 4(m t i ,y t
i,j )and the correlation between the image emotion and the
user’s intimacy with friends f 5(y t i ,y t
j ,μt i,j ).
?Users’demographics correlation f 6(p i ,y t
i,j ).It de-notes the correlation between the image emotion and the users’demographics information,which is formal-ized as three vectors gender,marital status and occupa-tion.
4.1.The predictive model
The input of the model is an image social network G ,and
the output of the model is the inference results Y .The cor-relations described above are instantiated as different factor functions.
(1)Visual features correlation function :
f 1(u t i,j ,y t
i,j )=1z α
exp {αT ·u t i,j }(2)where u t i ,j represents the visual features and y t
i,j repre-sents the emotion of image x t i,j .(2)Temporal correlation function :
f 2(y t i ,y t
i )=1z ε
exp {εi ·g (y t i ,y t i )},t i represent the emotion of user v i at time t and t .Function g (y t i ,y t i )is used to depict the correla-tion. (3)Social correlation function : f 3(s t i ,y t i,j )=1z γ exp {γT ·s t i }(4)where s t i denotes the number of user’s friends. f 4(m t i ,y t i,j )=1z δ exp {δT ·m t i }(5)where m t i denotes the major emotion of the user’s friends. f 5(y t i ,y t j ,μt i,j )=1z η exp {ηi,j ·h (y t i ,y t j ,μt i,j )}(6)where y t i and y t j represents the emotions of user v i and v j at time t and μt i,j measures the intimacy between them at time t ,which is calculated from their interaction fre- quency.Function h (y t i ,y t j ,μt i,j )is used to depict the cor-relation. (4)Users’demographics correlation function : f 6(p i ,y t i,j )=1z β exp {βT ·p i }(7) where p i denotes the user’s demographics information,namely,gender,marital status and occupation. Given the above factor functions,we de?ne the joint dis-tribution of the model:P (Y |G )= 1Z x t i,j f 1(u t i,j ,y t i,j ) x t i,j y t i f 2(y t i ,y t i ) x t i,j f 3(s t i ,y t i,j ) x t i,j f 4(m t i ,y t i,j ) x t i,j v j f 5(y t i ,y t j ,μt i,j ) x t i,j f 6(p i ,y t i,j )= 1 Z exp {θT S }(8) where Z =Z αZ εZ βZ γZ δZ ηis the normalization term,S is the aggregation of factor functions over all nodes,θdenotes all the parameters,i.e.,θ={α,β,γ,δ,εi ,ηi,j }. Therefore the target of modeling is to maximize the log-likelihood objective function ?=log P (Y |G ). 4.2.Model learning Given the model’s input and output,next we’ll detail the learning process of the model and the algorithm is summa-rized in Algorithm 1. The objective function can be rewritten as:?=log P (Y |G )=log Y |Y U exp {θT S }?log Z =log Y |Y U exp {θT S }?log Y exp {θT S } (9) Thus the gradient of θcan be represented as:???θ=?(log Y |Y U exp {θT S }?log Y exp {θT S })?θ =E p θ(Y |Y U ,G )S ?E p θ(Y |G )S (10)The algorithm updates the parameters by θ=θ0+?? ?θ·λ. 5.EXPERIMENTS 5.1.Experimental setup Data set.The raw data set we employed and the way we es-tablish the ground-truth are described in the observations sec-tion.In order to examine the performance of every emotion category,we evenly and randomly pick out 11,500images from every emotion category and 69,000images are chosen in total,60%for training and 40%for testing. Herein,we adopt the method proposed by Wang [13]to extract the visual features,including the color theme,satura-tion,brightness,etc.In total we extract 25features and the effectiveness of these features is con?rmed in [12],[5]. Comparison methods.We conduct performance com-parison experiments to demonstrate the effectiveness of our Table1.The F1-Measure of the emotion inference. Method Happiness Surprise Anger Disgust Fear Sadness NB0.2660.0820.0580.2910.1450.275 SVM0.2940.1290.0880.3250.2330.286 FGM0.4330.3610.2950.4470.3790.434 D-FGM0.4510.4010.3340.4630.4160.442 model.Three existing methods,namely,Naive Bayesian (NB),Support Vector Machine(SVM)and traditional factor graph model(FGM)are used for comparison. NB:Naive Bayesian is a widely used classi?er and achieves good performance[2].It is also used as the base-line method in[1].We use the Naive Bayesian tool provided by MATLAB4. SVM:SVM is a frequently-used method in many classi-?cation problems.The method is also used as the baseline method in[1],[5].Herein we use LIBSVM design by Chang and Lin5. FGM:This method is used in[5]to infer emotions of images.A partially-labeled factor graph model is utilized as a classi?er. Evaluation metrics.We compare the performance of our proposed model with three baseline methods in terms of pre-cision,recall and F1-Measure.These evaluation metrics are widely used in the classi?cation problems[12]. 5.2.Experimental results Due to the limit of the length of the paper,herein we just exhibit the F1-Measure in Table1on the behalf of the evalu-ation metrics.As shown in the table,our model signi?cantly enhances the performance.The average F1-Measure reaches 0.420,increased by23.4%compared with Naive Bayesian, 19.4%compared with SVM and2.4%compared with FGM. NB and SVM are only capable of handling vectors.In this problem the vectors contain the visual features,the users’de-mographics and parts of the social attributes(the number of the user’s friends and the major emotion of the user’s friends). However,these two models cannot handle the correlations be-tween images,which are instantiated as edges in FGM and D-FGM.As a result they let go of the temporal correlation and the intimacy with the user’s friends.As for FGM,it can model the vectors and edges jointly.However,all the edges are of the same weight in FGM,so though the model can take the correlations between images into consideration,it still cannot model the differences between edges.This draw-back hurts the performance and lets go of some important at-tributes,such as the user’s intimacy with friends,where the in-timacy is modeled as the weight of the edges.On the contrary, the proposed D-FGM can model the vectors and the weighted edges together,so it better depicts the image social network and achieves the best performance. 4A widely used software developed by MathWorks,Inc. 5A library for support vector machines. Fig.2.F1-Measure of different factor combinations. 5.3.Factor contribution analysis In our work,we utilize the information of the users’demo-graphics and introduce them into a factor graph model as fac-tor functions.Wondering whether these factors bene?t the inference,we investigate the contribution of every factor in the model.Every time we take each of the factors out of the primitive model and examine the performance while the other factors remain the same. The experimental results evaluated by F1-Measure are vi-sualized in Figure2.The model involving all factors achieves the best performance in all emotion categories,which vali-dates the effectiveness of the factors.Other interesting results are summarized as follows. ?When inferring disgust and surprise,the gender infor- mation bene?ts the inference remarkably(+6.7%for disgust and+2.8%for surprise). ?When inferring happiness and anger,the occupation in- formation really matters.The F1-Measure increases by 1.9%when inferring happiness and3.7%when infer- ring anger. ?When inferring fear,the marital status information is very useful by showing5.9%improvement. ?However,interestingly,when inferring sadness,the gender and occupation information makes little help, and the marital status information helps slightly (+1.0%),which indicates that the perception of sadness is mainly determined by the visual features. The results also correspond to the observations we de-scribed before,which veri?es the rationality of introducing the users’demographics into the modeling of inferring emo-tions from social images. 5.4.Case study In the above investigation we discover that different users’de-mographics result in different emotion perception of images. Table2details the analysis by reporting the labeled emotion, the visual features and the users’demographics of several im-ages.Two images on the left depict the same scene and their visual features are quite alike.However,we?nd out that the image on the top is uploaded by a female named bekahpaige on Sept,6th,2003,who labels this image as happiness and the image on the bottom is uploaded by a male named54rf on Apr,17th,2009,who labels this image as surprise.The gender difference in human emotion perception is veri?ed by the behavioral study[8].The difference can be explained that Table2.Different users’demographics result in different emotion perception of images. Image& Emotion tags written by the uploader Visual features User’s demo- graph- ics Image& Emotion tags written by the uploader Visual features User’s demo- graph- ics Image& Emotion tags written by the uploader Visual features User’s demo- graph- ics Happiness Female Single Artist Sadness Male Single Engi- neer Happiness Male Artist Surprise Male Single Artist Happiness Male Taken Engi- neer Disgust Male Engi- neer males are likely to be less aware of their surroundings,and thus the rare occasion of noticing the daily sunrise?lls the male’s heart with surprise,while the more observant female is simply happy with the pleasant phenomenon.Similarly,two images in the middle both capture the blossom of?owers,but the top one expresses sadness by a single user akshaydavis on Apr,20th,2008and the bottom one conveys happiness by a taken user davidhelan on Jul,20th,2005,indicating that sin- gle users and taken users have different emotion perception. The images on the right demonstrate the different emotion perception between engineers and artists. 6.CONCLUSIONS In this paper,we study the problem of“link inferring with users’demographics”for understanding the emotions behind social images.First we investigate whether users’demo- graphics relate to image emotions on social networks.Then by introducing these patterns as factor functions into model- ing,we propose a factor graph model called D-FGM which can not only infer emotions from social images by the visual features,but also by the users’demographics.Experiments on the world’s largest image sharing website Flickr validate the effectiveness of our model. 7.ACKNOWLEDGEMENTS This work is supported by the National Basic Research Pro- gram of China(2012CB316401),National Natural,and Sci- ence Foundation of China(61370023).This work is partially supported by the National Basic Research Program of China (2011CB302201),and the National High Technology Re- search and Development Program(“863”Program)of China (2012AA011602).We would also like to thank Microsoft Research Asia-Tsinghua Univertity Joint Laboratory:FY14- RES-SPONSOR-111for its support. 8.REFERENCES [1]J.Tang,Y.Zhang,J.Sun,J.Rao,W.Yu,Y.Chen,and ACM Fong,“Quantitative study of individual emotional states in social networks,”IEEE Transactions on Affec- tive Computing(TAC),vol.3,pp.132–144,2012. 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[7]H.Huang,J.Tang,S.Wu,L.Liu,and X.Fu,“Mining triadic closure patterns in social networks,”in World Wide Web Conference(WWW),2014,pp.499–504. [8]A.H.Fischer,P.M.Rodriguez Mosquera,A.E.van Via- nen,and A.S.Manstead,“Gender and culture differ- ences in emotion,”Emotion,vol.4,pp.87–94,Mar. 2004. [9]P.Ekman,“An argument for basic emotions,”Cognition and Emotion,vol.6,no.3-4,pp.169–200,1992. [10]L.Xie,“Picture tags and world knowledge,”in ACM Multimedia,2013. [11]S.J.Hwang,“Discriminative object categorization with external semantic knowledge,”2013,Ph.D.Disserta- tion,The University of Texas at Austin. [12]Y.Yang,J.Jia,S.Zhang,B.Wu,Q.Chen,J.Li,C.Xing, and J.Tang,“How do your friends on social media dis- close your emotions?,”in AAAI Conference on Arti?cial Intelligence(AAAI),2014,pp.306–312. [13]X.Wang,J.Jia,J.Yin,and L.Cai,“Interpretable aes- thetic features for affective image classi?cation,”in ICIP,2013,pp.3230–3234. 幼儿园常用英语口语100句 (一)打招呼，问好。 1. Good morning? Good morning! 早上好,早上好～ 2. Good afternoon? Good afternoon! 下午好,下午好～ 3. How old are you? I am (four). 你几岁了,我(四)岁。 4. What is your name? My name is (lanlan) 你叫什麽名字,我叫(兰兰)。 5. See you tomorrow? See you tomorrow. 明天见,明天见。 6. Are you OK? Yes, I am OK. 你还好吗, 我挺好的。 7. Hi, how are you? I am fine, thank you. 嘿，你好吗,我很好，谢谢。 8. Excuse me! 打扰一下～ 9. Bye-bye mum / daddy. 再见妈妈 / 爸爸。 10. Good-bye 再见。 11. Excuse me, what is your name? 打扰一下，你叫 什麽名字, 12. See you next week. 下周见。 (二)表扬，感谢。 1. You are beautiful! 你真漂亮～ 2. You are clever! 你真聪明～ 3. You are good! 你真棒～ 4. Great! / Good! / OK! 好。 5. Very nice! 非常好～ 6. Happy New Year! 新年快乐～ 7. Happy birthday! 生日快乐～ 8. Merry Christmas! 圣诞快乐～ 晋灵公不君（宣公二年） 原文： 晋灵公不君。厚敛以雕墙。从台上弹人，而观其辟丸也。宰夫胹熊蹯不熟，杀之，寘诸畚，使妇人载以过朝。赵盾、士季见其手，问其故而患之。将谏，士季曰：“谏而不入，则莫之继也。会请先，不入，则子继之。”三进及溜，而后视之，曰：“吾知所过矣，将改之。”稽首而对曰：“人谁无过？过而能改，善莫大焉。诗曰：‘靡不有初，鲜克有终。’夫如是，则能补过者鲜矣。君能有终，则社稷之固也，岂惟群臣赖之。又曰：‘衮职有阙，惟仲山甫补之。’能补过也。君能补过，衮不废矣。” 犹不改。宣子骤谏，公患之，使鉏麑贼之。晨往，寝门辟矣，盛服将朝。尚早，坐而假寐。麑退，叹而言曰：“不忘恭敬，民之主也。贼民之主，不忠；弃君之命，不信。有一于此，不如死也！”触槐而死。 秋九月，晋侯饮赵盾酒，伏甲将攻之。其右提弥明知之，趋登曰：“臣侍君宴，过三爵，非礼也。”遂扶以下。公嗾夫獒焉。明搏而杀之。盾曰：“弃人用犬，虽猛何为！”斗且出。提弥明死之。 初，宣子田于首山，舍于翳桑。见灵辄饿，问其病。曰：“不食三日矣！”食之，舍其半。问之，曰：“宦三年矣，未知母之存否。今近焉，请以遗之。”使尽之，而为之箪食与肉，寘诸橐以与之。既而与为公介，倒戟以御公徒，而免之。问何故，对曰：“翳桑之饿人也。”问其名居，不告而退。——遂自亡也。 乙丑，赵穿①攻灵公于桃园。宣子未出山而复。大史书曰：“赵盾弑其君。”以示于朝。宣子曰：“不然。”对曰：“子为正卿，亡不越竟，反不讨贼，非子而谁？”宣子曰：“呜呼!‘我之怀矣，自诒伊戚。’其我之谓矣。” 孔子曰：“董狐，古之良史也，书法不隐。赵宣子，古之良大夫也，为法受恶。惜也，越竞乃免。” 译文： 晋灵公不行君王之道。他向人民收取沉重的税赋以雕饰宫墙。他从高台上用弹弓弹人，然后观赏他们躲避弹丸的样子。他的厨子做熊掌，没有炖熟，晋灵公就把他杀了，把他的尸体装在草筐中，让宫女用车载着经过朝廷。赵盾和士季看到露出来的手臂，询问原由后感到很忧虑。他们准备向晋灵公进谏，士季说：“如果您去进谏而君王不听，那就没有人能够再接着进谏了。还请让我先来吧，不行的话，您再接着来。”士季往前走了三回，行了三回礼，一直到屋檐下，晋灵公才抬头看他。晋灵公说：“我知道我的过错了，我会改过的。”士季叩头回答道：“谁能没有过错呢？有过错而能改掉，这就是最大的善事了。《诗经》说：‘没有人向善没有一个开始的，但却很少有坚持到底的。’如果是这样，那么能弥补过失的人是很少的。您如能坚持向善，那么江山就稳固了，不只是大臣们有所依靠啊。 英语口语技能训练练习题 1 1. Do you have a hobby? Can you tell us something about it? What do you like to do in your spare time? 2. How do you usually spend your weekends? 3. What kind of books do like best and why? 4. What kind of music do you like best and why? 5. Do you like watching English TV programs and why? 6. Do you like traveling and why? 7. What kind of movies do you like best and why? 2 1. Do you think it is important for university students to do physical exercise regularly and why? 2. What is your favorite way of keeping fit and why? 3. Do you think going on a diet is (also) a good way to keeping fit? 4. What kind of physical exercise do you think is most suitable for elderly people and why? 3 1. What do you enjoy doing in your spare time? 2. What aspect of your study do you find most difficult? 3. What do you usually do during your vacations? 4. Can you say something about your classmates? 5. What’s your major? Why did you choose it? 6. Which course do you like most and why? 7. How do you support your study financially? 8. Can you say something about the entertainment available on your campus? 4 1. How do you usually keep in touch with your friends? 2. Which way of corresponding do you think will be most popular in the near future? 3. Do you think letters will eventually be replaced by email or fax? Please give your reasons. 4. What do you think is the most efficient way for people to keep in touch with each other and why do you think so? 5 1. How do you like university life? Which aspect of university life do you find most interesting (stimulating /challenging /exciting /enjoyable)? 2. How are you getting along with your classmates? 3. You’ve already finished quite a number of courses in the university. Which course do you think is most interesting? Why? 4. How do you like the food in the dining halls? 5. What do you usually do on weekends? （一）打招呼，问好。 1. Good morning? Good morning! 早上好？早上好！ 2. Good afternoon? Good afternoon! 下午好？下午好！ 3. How old are you? I am (four). 你几岁了？我（四）岁。 4. What is your name? My name is (lanlan) 你叫什麽名字？我叫（兰兰）。 5. See you tomorrow? See you tomorrow. 明天见？明天见。 6. Are you OK? Yes, I am OK. 你还好吗？我挺好的。 7. Hi, how are you? I am fine, thank you. 嘿，你好吗？我很好，谢谢。 8. Excuse me! 打扰一下！ 9. Bye-bye mum / daddy. 再见妈妈/ 爸爸。 10. Good-bye 再见。 11. Excuse me, what is your name? 打扰一下，你叫什麽名字？ 12. See you next week. 下周见。 （二）表扬，感谢。 1. You are beautiful! 你真漂亮！ 2. You are clever! 你真聪明！ 3. You are good! 你真棒！ 4. Great! / Good! / OK! 好。 5. Very nice! 非常好！ 6. Happy New Year! 新年快乐！ 7. Happy birthday! 生日快乐！ 8. Merry Christmas! 圣诞快乐！ 9. Thank you very much! 非常感谢你！ 10. Wonderful! 太棒了！ 11. I am the winner! 我是胜利者！ 12. I am the first! 我是第一名! 13. You are beautiful! 你真漂亮！ 14. You are welcome! 欢迎你！ 15. How beautiful! 多美呀！ 16. How funny! 多有趣呀！ 17. Wow! It’ｓso nice! 哇！它太好了。 18. It’ｓ a nice day! 这是一个很好的一天！ 19. Oh, my god / goodness! 哦，我的上帝！ 20. Wow! It’ｓso nice / lovely! 哇！它太棒了/ 太可爱了！ 21. Nice girl / boy. 好女孩/ 男孩。 22. You are so sweet. 你是非常可爱的。 23. You look very smart. 你看起来非常聪明。 24. I wish you a Merry Christmas！我祝你圣诞快乐！ 浅谈幼儿口语的发展 幼儿期语言的发展，重在口语表达，而口语表达就是俗说的让孩子"能说会道"。孩子口语能力发展的好坏与否关系到他们的健康成长,因此,我们必须把握好时机,培养孩子说话兴趣，养成良好的口语表达习惯，让孩子想说、要说、愿意说。它需要家园双方的共同配合，努力协作，并及时有效地进行引导和鼓励。怎样才能提高其口语表达能力？在幼儿园，教师应以哪几方面着手呢？ 第一、循序渐进，发展口语，培养其口语表达能力 (一)、看练结合，讲清词意 幼儿期是人的一生中词汇量增加最快的时期。幼儿掌握词汇的多少和对词的理解程度，直接影响着他们的口语表达能力。因而在口语训练中，应多种手段结合，让他们从实物、图片、演示、动作、表情等具体形象中理解词的意思，表达自己的看法。 1、看练结合，讲清关键词语。如讲《小马过河》时，我用小马、老马、老牛、小松鼠等直观教具及演示法进行讲解。孩子们边听故事边看演示，很快弄懂了关键用词"趟、甩"等词义。故事讲完后，我问"趟 "是什么意思？孩子们说："就是从浅水里走过去。""甩"是什么意思？幼儿说："因为小马有尾巴，'甩'就是 摇尾巴。"孩子们看了我的演示，所以能化难为易，比较正确地理解词意。 2、演练结合，讲清动词含义。 例如：在《老虎拜师》的故事里动词较多，我边讲故事边做动作。故事讲完后，我就将"眯、转、跑、扑、捉、追、爬、跳"等动作一一做出来。然后由我做动作，幼儿说出相应的动词，再让幼儿之间互相交流。这样，经过反复练习，幼儿基本上能正确地运用口语准确地表达出来。 3、观察实景，帮助幼儿理解词意。 如《小蝌蚪找妈妈》的故事，我们用自然角养的一些小蝌蚪，让幼儿观察蝌蚪游动的情景，帮助他们理解"游来游去"的意思。孩子们观察并理解了这个词，等我再问"游来游去"是什么意思？幼儿齐声回答："就是游过来游过去。"接着我又告诉他们："…来…去"这个词还可以用在"跑来跑去，跳来跳去"等等，孩子们则更聪明，补充了很多词组，如"蹦来蹦去，飞来飞去，说来说去"等等，这样不但掌握了"…来…去"的用法，还扩大了词汇量，发展口语表达能力。 （二）、利用多种途径，发展口语 《学生口语表达能力的训练》课题研究实施方案 一、课题研究的现实背景意义 1.《新课程标准》提出：“学生应具有日常口语交际的基本能力，学会倾听、表达与交流，初步学会运用口头语言文明地进行人际沟通和社会交往。”而训练学生口语表达，是培养学生日常口语交际能力的重要手段。 2.当今社会是知识爆炸的时代，是信息的时代，社会交往频繁，生活节奏加快，时间观念增强，各项工作越来越讲求效率。在日常生活中，口语比书面语更常用、更重要。各行各业、各个领域都离不开口语。因此，培养和提高学生的口语表达能力，是语文教学的一项重要任务。 3.《诗经》中说：“辞之辑矣，民之洽矣；辞之怿矣，民之莫矣。”意思是说表达能做到言之有礼，言之有情，老百姓就和睦安定。 4.本校实际：我执教六年级语文，在平时的语文课堂教学中，常发现这样的现象：学生的口语表达能力欠缺，即使回答了老师或者同学提出的问题，表达的也不好。虽然学生们发言积极性很高，但缺乏的是较强的口语表达能力。学生在口语表达方面存在较突出的问题有：①言之无物。在进行口语交际时，由于缺乏生活材料和语言材料的积蓄，造成思维的狭隘性或单一性，影响语言的流畅性或丰富性，学生有的结结巴巴，有的无话可说，有的人云亦云，使原本能体现个性和创造性的口语交际变得枯燥无味，无话可说。②言之无序。学生的表达缺乏条理性，即使有内容想表达，也不知该怎么说。这主要是源于孩子们平时缺少应有的训练，不懂得如何在表达中抓住要点，有条理地说。③言之无胆。有些学生胆子小，一旦走上台，就扭扭捏捏，脸涨得通红，不知所云，这在很大程度主，是由于缺少当众说话的锻炼机会，没有交流展示的习惯，更没有交际成功的喜悦与满足，造成交际时心理紧张。④不善倾听。口语交际是双向互动，可课堂上我们经常发现孩子犯这样的错误：他们容易以自我为中心，急于表达自己的想法，但是当别的同学在说的时候，欠缺应有的礼仪，往往不注意仔细听，更别说抓住别人表达的中心了。我校地处乡镇，学生都是农村儿 家有儿女的家长们，如果你家中有宝宝学英文的话，那么对照以下10个阶段，就能清楚地知道孩子的英语口语水平处在哪个级别了，瑞思学科英语资深教育专家认为，孩子学英语，要从兴趣、热情入手，然后不断练习演讲演示，点滴各界才能过到“口语之王”的水平。 1. Enthusiasm 1. 积极热情 万事开头难，有了兴趣和热情，学习英语口语不再是难事。 2. Obsession 2. 沉迷其中 凡事沉浸其中，才能最终学到真东西，少儿英语学习也如是。 3. Discomfort 3. 略有不安 在英语学习路上，总有坎坷和崎岖之时，克服之后便是坦途。 4. Shyness 4. 羞于开口 总有一段时间是不敢开口说英语的时候，但只要张口，在接下来的日子里，英语学习就不难了。 5. Lack of Understanding 5. 缺乏理解 学习英语，如果全都理解了才能学通的话，就大错特错了，以有知来推测未知，才是学英语的最佳方法。 6. Frustration 6. 挫折 凡事都不是一帆风顺的，学习英语也如此。 7. Revelation 7. 新发现 在学习的路上，总有新发现，新刺激，这样才能一直激励着你一路向前。 8. Excitement 8. 兴奋 学习路上不仅仅是挫折和羞于开口，还会有新奇和兴奋常伴左右。 9. Embarrassment 9. 尴尬 不要害怕出丑，做任何事都有尴尬的时候，克服之后就能一路向前。 10. Triumph! 10. 获得成功！ 英语学习的最后一站，也是终极目的就是获得成功，掌握并精通这一语言才是学 习的最终目标哦。 各位家长可以对照这10个阶段，检测一下孩子处在哪个等级上呢？其实大部分的孩子可能处于第3阶段之前，而造成这个的主要原因就是“羞于开口”。因为总是担心自己的发音不标准，所以干脆就不说了，这使得我们的英语学习止步不前~学习英语自信心很重要，有了自信，相信你也能侃侃而谈，顺利走向第10个完满阶段，来一个华丽的转身，成为那个惊艳四座的人~ 关于瑞思学科英语 2007年进入中国，率先从事学科英语教育培训，3-18岁少儿英语培训专家，其“浸入式学科英语”课程体系涵盖3～6岁幼儿英语、7～12岁儿童英语、13～18岁青少儿英语，并注重培养项目管理、演讲演示和团队合作三大能力。 每天，全国80多个城市250多家校区10万多名学员在瑞思同步学习； 每年，数十万家庭选择瑞思，九成以上的家庭选择持续续费，让孩子一直在瑞思学习，直至进入美国名校。 如何翻译古文 学习古代汉语，需要经常把古文译成现代汉语。因为古文今译的过程是加深理解和全面运用古汉语知识解决实际问题的过程，也是综合考察古代汉语水平的过程。学习古代汉语，应该重视古文翻译的训练。 古文翻译的要求一般归纳为信、达、雅三项。“信”是指译文要准确地反映原作的含义，避免曲解原文内容。“达”是指译文应该通顺、晓畅，符合现代汉语语法规范。“信”和“达”是紧密相关的。脱离了“信”而求“达”，不能称为翻译；只求“信”而不顾“达”，也不是好的译文。因此“信”和“达”是文言文翻译的基本要求。“雅”是指译文不仅准确、通顺，而且生动、优美，能再现原作的风格神韵。这是很高的要求，在目前学习阶段，我们只要能做到“信”和“达”就可以了。 做好古文翻译，重要的问题是准确地理解古文，这是翻译的基础。但翻译方法也很重要。这里主要谈谈翻译方法方面的问题。 一、直译和意译 直译和意译是古文今译的两大类型，也是两种不同的今译方法。 1．关于直译。所谓直译，是指紧扣原文，按原文的字词和句子进行对等翻译的今译方法。它要求忠实于原文，一丝不苟，确切表达原意，保持原文的本来面貌。例如： 原文：樊迟请学稼，子曰：“吾不如老农。”请学为圃。子曰：“吾不如老圃。”（《论语?子路》） 译文：樊迟请求学种庄稼。孔子道：“我不如老农民。”又请求学种菜蔬。孔子道：“我不如老菜农。”（杨伯峻《论语译注》） 原文：齐宣王问曰：“汤放桀，武王伐纣，有诸？”（《孟子?梁惠王下》） 译文：齐宣王问道：“商汤流放夏桀，武王讨伐殷纣，真有这回事吗？（杨伯峻《孟子译注》） 上面两段译文紧扣原文，字词落实，句法结构基本上与原文对等，属于直译。 但对直译又不能作简单化理解。由于古今汉语在文字、词汇、语法等方面的差异，今译时对原文作一些适当的调整，是必要的，并不破坏直译。例如： 原文：逐之，三周华不注。（《齐晋鞌之战》） 译文：〔晋军〕追赶齐军，围着华不注山绕了三圈。 浅析大学生英语口语能力的发展 赵钧,唐冉菲,王亚娟 (首都医科大学卫生管理与教育学院,北京 100069) 【摘要】发展大学生英语的口语能力不仅是大学英语教学大纲对大学生英语学习的要求,也是时代和社会发展的要求,更是语言学习的根本要求。笔者在本文中首先谈到了发展大学生英语口语能力的必要性;继而阐述了目前大学生英语口语能力的现状并剖析了影响他们英语口语发展的三个因素;最后具体论述了如何从这三因素入手,加强学生的口语能力,让学生尽快地形成良好的口语能力,从而完整地掌握英语这门语言。 【关键词】口语能力;动机水平;焦虑 【中图分类号】H3 【文献标识码】J 【文章编号】09-63 一、发展大学生英语口语能力的必要性 众所周知,语言是人类交流的一个重要形式。那么具体来说,什么是“语言”呢?胡壮麟教授主编的《语言学教程》中是这样来下定义的:“Language is a system of arbitrary vocal systems used for human communication”,“语言就是用来交际的,具有任意性的,有声的符号系统”。显然,这其中核心的内容的就是,语言不仅是用来交际的,而且还是有声的。这说明在语言中,口语的发展是非常重要的。据相关调查表明,成人在日常交际活动中的听,说,读,写得比例是45%,30%, 16%和9%。由此可见,听说能力在日常的交际活动中占有重要的位置。 大学生学习英语的一个重要目的是能够用英语口语自由地与别人进行沟通。这不仅是大学英语教学大纲对学生英语学习的要求,更是语言学习的根本需求。实际上,不能运用语言来自由地交际沟通,就不能算作完整地掌握了一门语言。随着国际交往的增加,大力发展大学生的口语能力已经变成了社会和时代发展的必然了。 在日常的教学中,笔者发现几乎所有的学生在英语学习中都表达出提高英语口语能力的愿望和要求。他们中的许多人虽然成功地通过了大学四六级的考试,但是距离用口语自由地交流还有相当的距离。对他们来说,读起来,写起来,听起来毫无困难的句子,段落,在用语言表达的时候,就会变得异常的困难。学生的口语的水平与他们的阅读,听力,写作的水平不成比例的发展在大学英语教学中是个普遍的现象,也是一个长期困扰学生和老师的问题。 所以,如何帮助学生摆脱“哑巴英语”的状态,能够让他们自信地,自由地用英语口语进行交际,让学生完整地掌握英语这门语言,实现语言学习的本质要求,是师生的共同的目的,也是大学英语教师迫在眉睫的任务。 那么,学生目前的英语口语水平是个什么状况,这种状况后面的原因是什么,笔者在下面就这两个问题进行阐述和分析。 二、大学生目前英语口语表达能力及其原因 (一)大学生目前的英语口语表达能力 笔者本学年教授的班级分别是,一个2008级的七年制班级和一个2007级的七年制班级。下面就是他们在本学年中英语口语考试中的成绩。 2007级七年制2008年六月期末口语考试分数段(10分满分) 分数8-106-84-62-40-2人数4122930所占比例3%5%6%63%% 8.20.4.0 幼儿常用英语口语汇总 幼儿学英语口语，重在增加输出，整句和简明口语表达，增加英语口语视频频率，都是提高幼儿英语口语高效的实用方法。瑞思学科英语为大家整理了幼儿常用口语和对话例句，方便小朋友们进行英语口语练习。 I．Reception入园接待 1．Hi！/ Hello！你好！ 2．Good morning．早上好。 3．Hello，nice to meet you．/ Nice to see you．你好，见到你真高兴。 4．How are you? I am fine，thank you．你好吗?我很好，谢谢。 5．Please say bye－bye to your mummy / daddy．请和妈妈/爸爸说再见。 6．Come in，please．请进。 7．Come on．/ come here，please．请过来。 8．Take off your coat．脱掉外套。 9． Please put on your morning check－card．请把晨检牌带好。 II．Group Activity集体活动 1．Are you ready? 准备好了吗? 2．Let’s start．让我们开始。 3．Who can tell me? / Who can answer me? 谁能告诉我?谁能回答? 4．Look at me．看着我。 5．Look at the blackboard．看黑板。 6．Be quite．/ Keep quite．保持安静。 7．Sit well．/ Sit nicely．坐好。 8．Put up your hand．/ Put down your hand．把手举起来/ 把手放下。9．Stop talking．别说话。 10．Is that clear? / Do you understand? 清楚了么?你明白吗? 11．Read with me．和我一起读。 12．Return to your seat．回座位。 13．Stand up．/ Sit down．起立/ 坐下。 14．Listen carefully．仔细听。 15．Listen to me．/ Listen to the music．听我说/ 听音乐。 16．Say it in English．用英语说。 17．Do you know? 你知道吗? 18．Let’s play a game．让我们来做游戏。 19．Let’s write / draw something．让我们来写点什麽/ 画点什麽。20．Let’s dancing / singing．让我们来跳舞唱歌。 21．Let’s listen to a story．让我们听个故事。 22．Let’s listen to the tape．让我们听磁带。 23．Let’s watch TV / a play．让我们看电视/ 看表演。 24．Let’s say it together．让我们一起说。 25．What did you hear? 你听到什麽了? 26．Who has finished? 谁做完了? 27．Who want to try? 谁想试试? 28．How do you know? 你怎麽知道的? 29．Which one do you like? 你喜欢哪一个? 30．Put your hands on your knees．把手放在膝盖上。31．Attention．注意。 32．You are right．你是正确的。 33．You are so good！你真棒！ 34．Paint it in red．把它涂成红色。 35．Open / close your book，please．打开/ 合上你的书。 鞌之战选自《左传》又名《鞍之战》原文：楚癸酉，师陈于鞌(1)。邴夏御侯，逢丑父为右②。晋解张御克，郑丘缓为右(3)。侯日：“余姑翦灭此而朝食(4)”。不介马而驰之⑤。克伤于矢，流血及屦2 未尽∧6)，曰：“余病矣(7)！”张侯曰：“自始合(8)，而矢贯余手及肘(9)，余折以御，左轮朱殷(10)，岂敢言病吾子忍之！”缓曰：“自始合，苟有险，余必下推车，子岂＿识之(11)然子病矣！”张侯曰：“师之耳目，在吾旗鼓，进退从之。此车一人殿之(12)，可以集事(13)，若之何其以病败君之大事也擐甲执兵(14)，固即死也(15)；病未及死，吾子勉之(16)！”左并辔(17) ，右援拐鼓(18)。马逸不能止(19)，师从之，师败绩。逐之，三周华不注(20) 韩厥梦子舆谓己曰：“旦辟左右！”故中御而从齐侯。邴夏曰：“射其御者，君子也。”公曰：“谓之君子而射之，非礼也。”射其左，越于车下；射其右，毙于车中。綦毋张丧车，从韩厥，曰：“请寓乘。”从左右，皆肘之，使立于后。韩厥俛，定其右。逢丑父与公易位。将及华泉，骖絓于木而止。丑父寝于轏中，蛇出于其下，以肱击之，伤而匿之，故不能推车而及。韩厥执絷马前，再拜稽首，奉觞加璧以进，曰：“寡君使群臣为鲁、卫请，曰：‘无令舆师陷入君地。’下臣不幸，属当戎行，无所逃隐。且惧奔辟而忝两君，臣辱戎士，敢告不敏，摄官承乏。” 丑父使公下，如华泉取饮。郑周父御佐车，宛茷为右，载齐侯以免。韩厥献丑父，郤献子将戮之。呼曰：“自今无有代其君任患者，有一于此，将为戮乎”郤子曰：“人不难以死免其君，我戮之不祥。赦之，以劝事君者。”乃免之。译文1：在癸酉这天，双方的军队在鞌这个地方摆开了阵势。齐国一方是邴夏为齐侯赶车，逢丑父当车右。晋军一方是解张为主帅郤克赶车，郑丘缓当车右。齐侯说:“我姑且消灭了这些人再吃早饭。”不给马披甲就冲向了晋军。郤克被箭射伤，血流到了鞋上，但是仍不停止擂鼓继续指挥战斗。他说:“我受重伤了。”解张说:“从一开始接战，一只箭就射穿了我的手和肘，左边的车轮都被我的血染成了黑红色，我哪敢说受伤您忍着点吧！”郑丘缓说:“从一开始接战，如果遇到道路不平的地方，我必定（冒着生命危险）下去推车，您难道了解这些吗不过，您真是受重伤了。”daier 解张说:“军队的耳朵和眼睛，都集中在我们的战旗和鼓声，前进后退都要听从它。这辆车上还有一个人镇守住它，战事就可以成功。为什么为了伤痛而败坏国君的大事呢身披盔甲，手执武器，本来就是去走向死亡，伤痛还没到死的地步，您还是尽力而为吧。”一边说，一边用左手把右手的缰绳攥在一起，用空出的右手抓过郤克手中的鼓棰就擂起鼓来。（由于一手控马，）马飞快奔跑而不能停止，晋军队伍跟着指挥车冲上去，把齐军打得打败。晋军随即追赶齐军，三次围绕着华不注山奔跑。韩厥梦见他去世的父亲对他说:“明天早晨作战时要避开战车左边和右边的位置。”因此韩厥就站在中间担任赶车的来追赶齐侯的战车。邴夏说:“射那个赶车的，他是个君子。”齐侯说: “称他为君子却又去射他，这不合于礼。”daier 于是射车左，车左中箭掉下了车。又射右边的，车右也中箭倒在了车里。（晋军的）将军綦毋张损坏了自己的战车，跟在韩厥的车后说: “请允许我搭乗你的战车。”他上车后，无论是站在车的左边，还是站在车的右边，韩厥都用肘推他，让他站在自己身后——战车的中间。韩厥又低下头安定了一下受伤倒在车中的那位自己的车右。于是逢丑父和齐侯（乘韩厥低头之机）互相调换了位置。将要到达华泉时，齐侯战车的骖马被树木绊住而不能继续逃跑而停了下来。（头天晚上）逢丑父睡在栈车里，有一条蛇从他身子底下爬出来，他用小臂去打蛇，小臂受伤，但他（为了能当车右）隐瞒了这件事。由于这样，他不能用臂推车前进，因而被韩厥追上了。韩厥拿着拴马绳走到齐侯的马前，两次下拜并行稽首礼，捧着一杯酒并加上一块玉璧给齐侯送上去， 青少儿口语专家：网络视频实时互动教学，足不出户即可和真人外教跨洋交流 VOT国际英语，免费体验在线外教一对一课程：https://www.wendangku.net/doc/9a984709.html,/R4K4vrv（报名网址） 孩子英语口语能力发展的几个阶段 这里仅想以口语的发展做一个例子，来看看孩子的口语需要达到什么水平，便可以和美国孩子同班上课。 口语的第一个阶段Beginning ELD level 开始用简单英语输出 用简单句或是词组问问题和回答 用图片，手势来复述故事 可以自主使用社交中的问候语 口语的第二个阶段便是Early intermediate ELD level 孩子可以听懂对话内容，但却不能作出合乎语法规则的回答 可以用简单的单词和词组来提问和回答（处在蹦词的阶段） 可以重复多步骤的指示 可以借助手势，图片来复述简单的故事或是对话 可以用简单句子重复故事或是Presentation的大意 可以表达简单的需求“May I get a drink?” 可以背诵儿歌，歌曲，小故事等 口语的第三阶段Intermediate ELD level 可以用简单句回答各种问题 可以集中精力听故事或是信息类的音频，能抓住其中的细节，并能有回复 能够用较为正确的语法和语音表述 参与同龄人或是成年人的就熟悉主题进行的社交对话（提问和回答） 复述故事，谈论学校的活动（使用一定量的描述类词汇） 对于高年级（初中以上）的孩子，词阶段则要求能就主题进行简短的presentations。 这里只就前三个阶段的小孩子的口语发展部分做了一个大致的解读。对于大孩子，或是后面的两个更高级别的口语要求，有兴趣的可以自行去阅读。 基本上可以从这里看出口语发展的几个趋势： 1，口语也需要积累，从最初的用手势，到蹦词，到词组，到简单句。。 2，从开头的不符合语法，只表达大意，到注重语法的正确表述 3，从简单的提问回答，到主题社交闲聊，再到学术的主题探讨和presentations。这是前三个阶段，先着手将这些做好，便在口语上达到了进入正常班入读的要求了。 学生口语交际能力的培养和训练方案 口语交际以培养学生语言表达和人际交流能力为目的，它源于生活用于生活，既是立文所要又是生活所必需，对人一生功莫大焉。怎样才能学生的口语交际能力呢？以下是培养和训练方案。 一、培养学生的口语交际能力，首先要抓住课堂。 （一）、课堂首先要做到充分准备。 课堂要做到四备——备学生、备方法、备主题、备应对。正确认识学生，把握他们课内知识的积累情况，预估学生的表现，了解他们课外资源的接触情况；课前充分交流，累积和分析共享经验，找到切合学情适合自我的方式方法；选题要精，考虑学情与目标相结合、主题与意义相结合、兴趣与实际相结合；加强课前预估，分析可能的生成，考虑有效的化解。 （二）、其次课堂教学中要注重激趣。 做到三设——设情境、设角色、设环节。情境是学习进入的钥匙，好情境的设定能牢牢勾住学生的心与眼睛；角色设定，让学生对照参与，走进去体验与感悟，使学生身临其境，自会潜移默化；一节课就是一首歌，前奏催情、高潮纵情、尾声引思，激动了、共鸣了、内化了、思考了，一首歌的意义就体现了，课堂正是如此，环节的明晰也就是知识与要求的潜化，也就是目标与学生的亲密接触，对于加强学生的理解与掌握意义重大。 （三）、课堂上要求师生广泛参与。 情感参与和交流参与是必须要注意的。学生是主体，学生学到是目的，教师要调动自己的情绪乐为，学生要由被调动到自觉 感染；克服不敢说，不怕说不好，牢固确立认识——只有说才能说得好，发动学生参与，喜欢说的使其努力说、有负担的让其放开说、不愿说的帮其反复说。把说作为主线贯穿始终。 二、培养学生的口语交际能力，教师要教给学生方法。 教师要教给学生思考的方法和表达的方法。达到的目的、对方的反应、对方的情感、语言用词的考量。表达的技巧与方法不外乎正说与反说、直说与曲说，效果与践行让学生观摩体验，切忌理论强加和教条灌输。 三、在课余中培养口语交际能力 在课余生活中有很多方面是贴近生活，易于表达的，这些可以用来锻炼学生的口语交际能力。如用口语给同学自己的家，家人，自己的兴趣、爱好；用口语给家里人讲述自己一天的所见所闻。而且可以设计一些口语家庭作业，让学生在家庭中进行口语交际训练。例如家庭作业“打电话”（模拟）：让孩子和妈妈“打”电话，爸爸做评委，然后再变化角色。 四、适时评价。 做到鼓励与启发相结合、指正与设问相结合。鼓励要有，提人兴趣催人奋进，但一味地鼓励容易使人固步自封，裹足不前。所以在鼓励中要佐以一定的情境启发，让学生带着被表扬的愉悦心情乘胜再思再说，达到说得更好，大家应注意——愉悦的心情下大脑转的更快。适时地指出学生说话中的不足，并能够有导向性地让学生带着点评的标准继续说敞开说，学生的说就会不断进步。知不足而进取，知高远而自谦，学之道，有问长学，杯不盈天地为小，是知也。 少儿英语口语【99句】 1. Hello! (How do you do?) 你好！ 2. How are you?-I’m fine. Thank you. and you? 你好吗？我很好。谢谢，你呢？ 3. Good morning / afternoon/evening/night. 早上好/下午好/晚上好/晚安。 4. Excuse me。(sorry. I’m sorry) 打搅一下(对不起/不好意思) 5. Thank you!谢谢你！ 6. You are welcome. 不用谢。 7. How are you today? 今天还好吗？ 8. Nice to meet you. 见到你很高兴。 9. What’s your name? 你叫什么名子啊？ 10. My name is×××. 我叫×××。 11. What can I do for you? 我能为你做点什么？ 12. What’s wrong with you? (What’sthe matter?)你怎么了？(出什么事了？) 13. It’s time for class. 该上课了。 14. Come in please. 请进。 15. Let’s get ready for class! 让我们准备上课吧！ 16. Line up please! 排队！ 17. Attention please! 立正！ 18. At ease. 稍息。 19. Turn left/right! 向左/右转！ 20. One bye one please.no pushing. 一个一个来。不要挤。 21. Let’s go back to the classroom. 让我们回教室去。 22. It’s time for (breakfast lunch supper/dinner) 该吃早餐了/该吃午餐了/该吃晚餐了 23. Please eat up. Take your time. 把它吃完。慢慢吃。 24. Would you like some rice！来点米饭吧！ 25. Help yourself. 请吃，别客气。 26. Please have some fish/vegetables. 吃点鱼/蔬菜吧。 27. Do you want anymore? 还要吗？ 28. Anything to drink? 喝点啥？ 29. I’d like to drink some milk! 我想喝点年奶！ 30. Today we are going to learn some new words.今天我们将学习一些新单词。 31. Who wants to try? 谁来试试？ 32. Let me try! 我来试试！ 33. It’s your turn. 轮到你了。 34. Don’t be afraid/shy! 不要害怕/不要害羞！ 35. Try your best! 尽力做/尽力试。 36. Do you understand? 明白了吗？ 《鞌之战》阅读答案（附翻译）原文及翻 译 鞌之战[1] 选自《左传成公二年（即公元前589年）》 【原文】 癸酉，师陈于鞌[2]。邴夏御齐侯[3]，逢丑父为右[4]。晋解张御郤克，郑丘缓为右[5]。齐侯曰：余姑翦灭此而朝食[6]。不介马而驰之[7]。郤克伤于矢，流血及屦，未绝鼓音[8]，曰：余病[9]矣！张侯[10]曰：自始合，而矢贯余手及肘[11]，余折以御，左轮朱殷[12]，岂敢言病。吾子[13]忍之！缓曰：自始合，苟有险[14]，余必下推车，子岂识之[15]？然子病矣！张侯曰：师之耳目，在吾旗鼓，进退从之[16]。此车一人殿之[17]，可以集事[18]，若之何其以病败君之大事也[19]？擐甲执兵，固即死也[20]。病未及死，吾子勉之[21]！左并辔[22]，右援枹而鼓[23]，马逸不能止[24]，师从之。齐师败绩[25]。逐之，三周华不注[26]。 【注释】 [1]鞌之战：春秋时期的著名战役之一。战争的实质是齐、晋争霸。由于齐侯骄傲轻敌，而晋军同仇敌忾、士气旺盛，战役以齐败晋胜而告终。鞌：通鞍，齐国地名，在今山东济南西北。 [2]癸酉：成公二年的六月十七日。师，指齐晋两国军队。陈， 列阵，摆开阵势。 [3]邴夏：齐国大夫。御，动词，驾车。御齐侯，给齐侯驾车。齐侯，齐国国君，指齐顷公。 [4]逢丑父：齐国大夫。右：车右。 [5]解张、郑丘缓：都是晋臣，郑丘是复姓。郤（x ）克，晋国大夫，是这次战争中晋军的主帅。又称郤献子、郤子等。 [6]姑：副词，姑且。翦灭：消灭，灭掉。朝食：早饭。这里是吃早饭的意思。这句话是成语灭此朝食的出处。 [7]不介马：不给马披甲。介：甲。这里用作动词，披甲。驰之：驱马追击敌人。之：代词，指晋军。 [8] 未绝鼓音：鼓声不断。古代车战，主帅居中，亲掌旗鼓，指挥军队。兵以鼓进，击鼓是进军的号令。 [9] 病：负伤。 [10]张侯，即解张。张是字，侯是名，人名、字连用，先字后名。 [11]合：交战。贯：穿。肘：胳膊。 [12]朱：大红色。殷：深红色、黑红色。 [13]吾子：您，尊敬。比说子更亲切。 [14]苟：连词，表示假设。险：险阻，指难走的路。 [15]识：知道。之，代词，代苟有险，余必下推车这件事，可不译。 [16]师之耳目：军队的耳、目（指注意力）。在吾旗鼓：在我们 幼儿口语表达能力的发展 幼儿期是儿童言语不断丰富的时期，是熟练掌握口头言语的关键时期，也是从外部言语逐步向内部言语过渡并初步掌握书面言语的时期。孩子只有真正学会了说话，才能自由地和人交往，同时也才能更好地接受成人传授的知识和经验。 在这几年幼教工作实践中，我认为语言教育应从创设良好的语言环境、发展幼儿语言表达能力这两个方面入手。 一、创设良好的语言环境 语言的学习，除了跟幼儿的个体差异有关以外，与语言交流环境也有密切的关系。一个自由、宽松的语言交往环境，可以使幼儿养成良好的语言交往习惯。那如何创设这个语言环境呢？在班上与幼儿交流时，一定坚持使用普通话，从孩子刚入园开始，就要给孩子树立良好的榜样。除了这些，还应该积极鼓励班里能说爱说的孩子多说多讲，起一个好的带动作用，并让孩子在平时的游戏中锻炼口语表达，支持鼓励孩子与老师，家长，同学们进行交流。在平时的生活中，也要坚持使用普通话，让孩子一直在这种良好的语言交流环境里，不断得熏陶，体验到交流的乐趣，让孩子爱上表达，善于交流。 二、口语表达能力的发展 儿童与成人的言语交际，往往仅限于回答成人提出的问题，有时也向成人提出一些问题或要求，所以主要是对话言语。到了幼儿期，随着儿童活动的发展，儿童的独立性大大增长，他们常常离开成人从事各种活动，从而获得自己的经验、体会、印象等。在与成人的交际中，他们渴望把自己的各种体验、印象等告诉成人，这样就促进了幼儿独白言语的发展。发展幼儿的口语表达能力，应考虑到从丰富幼儿的词汇量和鼓励幼儿说完整话这两方面进行。 1.丰富幼儿的词汇量。影响幼儿口语表达能力的其中一个重要因素，就是孩子词汇量的多少。幼儿词汇量的增加，基本没有什么窍门可言，这要靠老师在平时的教学中使幼儿的词汇量得到丰富，还要及时把孩子们学会的词进行分类整理，在教学过程中循序渐进的渗透。例如：在《小老鼠》儿歌里的几个动词：小老鼠躲在桌子下面，小老鼠爬到灯台上，小老鼠想从上面跳下来，边教边做动作，使孩子初步理解每个词的意思，举一反三，让孩子们边玩边学，丰富了词汇的同时，也锻炼了口语表达的能力。 2.鼓励孩子完整的表达。幼儿不仅需要掌握丰富的词汇，还要学会运用正确的语言来表达自己的想法，从完整的说一句话，到连贯的说一段话，使幼儿的口语表达能力逐步得到提高，从孩子入园，就要鼓励幼儿学说完整话。对于口语表达能力较差的幼儿，就利用游戏来耐心引导，如：在角色游戏《爱心医院》和《百货商场》利用具体问题引导幼儿的思维，“你想买什么呀？”“我想买……”，“你哪里不舒服了？”“我肚子痛……”等等。另外，还可以利用创编诗歌的方法，让幼儿把话说得更加连贯、流畅。如仿编诗歌《云彩和风儿》，通过欣赏优美的诗歌，幼儿对诗歌产生了兴趣，抓住幼儿这一学习的契机，引导幼儿初步学会按诗歌的格式，创编自己想到的云彩的样子。如，有些幼儿编到：天上的风儿真能干，天上的云彩真有趣。风儿吹呀吹，云彩变成一只小蝴蝶，蝴蝶飞呀飞，飞到花丛中找朋友；吹呀吹，云彩变成小青蛙，小青蛙呱呱叫，想要捉害幼儿园常用英语口语100句
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