REGRESSION MODELS FOR RELEV ANCE FEEDBACK AND FEATURE SELECTION IN CONTENT-BASED IMAGE RETRIEV AL
Eric J.Pauwels
PNA4,CWI Amsterdam SJ1098,NL eric.pauwels@cwi.nl
Geert Caenen
ESAT-PSI,K.U.Leuven
B-3001Leuven,Belgium geert.caenen@esat.kuleuven.ac.be
1.INTRODUCTION
One of the main reasons why content-based image retrieval(CBIR)is such a challenging problem has to do with the fact that there is no canonical way to capture the visual content encapsulated in an im-age.As a consequence,most CBIR search-engines keep the human in the loop by regularly requesting his feedback to expedite the search-action.More precisely,the feedback is harnessed to estimate for every image in the database,the likelihood of its relevance to the user,whereupon the most promis-ing candidates are displayed for further inspection and feedback.This procedure is then iterated as often as necessary to locate the target image.
To model the fuzzy state of knowledge about the user’s preferences,the search engine assigns to ev-ery image in the database a relevance probability that re?ects the cur-
rent estimate of relevance.Hence,means that in its current estimate,the image is consid-ered to be highly relevant,whereas ex-presses the opposite.
Initially every image in the database is consid-ered equally likely.However,as more information about the user preferences becomes available (through relevance feedback),the probability measure will start concentrating on regions in the database that seem promising,and images in these regions are more likely to be sampled for display.
Relevance feedback mechanism:Most recent CBIR-interfaces have shifted their focus in feed-back from features to images:The user is no longer prompted to specify individual feature-values or-weights,but can directly express his (global)preference of one image relative to the rest.It is this model of natural interaction that we
lating the qualitative user feedback(in terms of ex-amples and counter-examples)into quantitative in-formation useful for retrieval.Furthermore,such models yield a principled tool to estimate the ef-?cacy of individual features in gauging the over-all relevance of images.As a consequence,it becomes possible to automatically and adaptively extract from the vast collection of pre-recorded image-features,the small subset that correlates best with the particular search at hand. Throughout this paper,we assume that ev-ery image can be represented by a-dimensional(numerical)feature-vector
.This also means that the rel-evance probability is in fact a function
of the feature-vector.
2.INFERENCE ENGINE BASED ON
LOGISTIC REGRESSION
2.1.Probabilistic Framework
At every stage of the search-history,the user in-spects a sample from the database(window1) and provides the system with feedback by mak-ing a number of positive and negative selections and transferring them into the collection box as examples and counter-examples(see above).This means that for the(say)images in the collection-box we have additional information that is cap-tured in a binary variable, where or0indicates that image was classi?ed as an example or counter-example,re-spectively.
To get a mathematical model we assume that these feedback-variables are observations of a bi-nary stochastic variable that assigns a relevance output(1or0)to every possible feature-vector .Put differently,given its current state of knowledge based on the examples and counter-examples in the collection box,is what the system predicts will be the user’s relevance judge-ment(example or counter-example)if the latter was asked to judge an image with feature-vector .We can model as a simple Bernoulli distri-bution(i.e.a binomial distribution with a single repetition)with success-probability,i.e.
Ber.The dependence of on the image-features is captured by equating the bino-mial success-probability to the relevance proba-bility introduced in section??.Hence,we get
Ber As a con-sequence,if then with a high probability and there is little uncertainty about the outcome.Similarly,if then
with a high probability.
Finally,we need to specify how the relevance-probability depends on the feature-values. Without loss of generality we can assume that this dependence takes the form of a logistic regression model:
(2) seems most suitable.Indeed,if relevance is pro-portional to the feature value,then a linear model will suf?ce.This is covered in this model by sim-ply putting.However,there obviously are situations where,for instance,only medium feature-values are acceptable,while extreme val-ues(both larger and smaller)are unacceptable. The quadratic model(with)is the simplest model that can handle this sort of qualitative dis-tinction.Determining the value for the parameters and is straightforward(see[?])using max-imum likelihood estimation,i.e.maximizing the
log-likelihood,where:
(3)
2.3.Automatic Feature Selection
In a realistic database-setting the number()of pre-computed numerical features for each image might be very large.However,most of the time only a small fraction of these features will be rel-evant for the particular search being executed at that time.Unfortunately,this subset will be dif-ferent for almost every new search and selecting which of these pre-computed features are most in-formative with regard to the user’s preferences,is key to achieving ef?ciency in retrieval.We will argue that one of the additional boons of the pro-posed approach is that regression comes with a set of diagnostic tools that allow the system to auto-matically quantify the goodness-of-?t of the pro-posed model and select the features accordingly. Indeed,if for a particular feature,the predic-tion of the?tted model(??)fails to square up with the relevance feedback from the user,this indicates that that particular feature does not contribute signi?cantly to the user’s perceptual appreciation. Conversely,if for a different feature,logistic regression yields a well-?tting model we can con-clude that the feature plays an important role in the user’s appreciation of the image,and we are well advised to bias the sampling-procedure as to favour feature-values that have high-value. That way,the fraction of relevant features in each new sample(as displayed on the sample display) will gradually increase.
The simplest way to measure the model’s per-formance is by looking at the value of the maxi-mum log-likelihood(??)that is achieved for each feature.Let denote this value for the-th fea-ture.It is easy to check that and that the deviance is a generalisation
of the sum of squared residuals.Hence,low(high) values for are indicative of good(poor)model-?t,or equivalently,high(low)predictive value for the feature.
This can immediately be put to good use in the computation of a global probability measure.In-deed,eq.(??)allows us to compute the probability for each individual feature.However,we need to combine the relevance-measures for different fea-tures into a global relevance probability for every image.A straightforward step is to assume inde-pendence of the different features so that the over-all probability can be obtained by multiplying all the individual contributions:
(4)
It is clear that in most cases,the assumption of in-dependence cannot be more than a convenient but rough?rst approximation.However,the structure of eq.(??)immediately suggests an interesting ex-tension in which the contribution of the different features are weighted relative to their importance:
(5) The weightfactors are determined automati-cally based on the deviance as .
From a practical point of view,automatic fea-ture selection(AFS),based on the weights, has the tremendous advantage that it relieves the database-designer from worrying which features to include in the database.Indeed,thanks to AFS it is advantageous to pre-compute as many and diverse features as possible,as irrelevant fea-tures will automatically be discarded by the AFS. This way the total feature-set will in all likeli-hood include for every conceivable query a(much smaller)subset—extracted by AFS—that cap-tures the search-objectives.
2.4.Extensions to Generalized Linear Models An additional advantage of the above regression models is that they can straightforwardly be ex-tended to accommodate more sophisticated feed-back https://www.doczj.com/doc/437877677.html,ck of space prevents us from go-ing into details,but the following examples illus-trate what we mean(a more detailed discussion can be found in[?]).For instance,once the num-ber of items in the collection-box exceeds certain thresholds,it becomes possible to?t genuinely more-dimensional models.Such models are able to capture interactions between various features. Furthermore,if there are more than two feedback categories(e.g.strongly positive,positive and neg-ative)then polytomous logistic regression will pro-vide an appropriate model.It is also possible to incorporate categorical features(e.g.keywords)in addition to the numerical ones.
In fact,all of the above extensions are members of the family of generalized linear models(GLIM,
cfr.[?])for which there exists a uni?ed estimation
theory.As a consequence,the principled approach to model?tting and automatic feature selection de-tailed above can be expanded to apply for these more general models.
3.RELATED WORK
There is a growing consensus among the CBIR-researchers that the use of both positive and negative feedback has distinct advantages over the use of positive examples alone.A number of recent papers report on the architecture of search-engines that are organised along these lines.Among these we mention the work done on non-parametric Bayesian density estimation in PicHunter(Cox et.al.[?])and the use of Support Vector Machines(Hong et.al.[?]).However,the methodology in these approaches is quite different from ours.We will restrict our discussion to two recent contributions that show some overlap with the work reported here.
Boosting individual features Thieu and Viola[?]?t a probabilistic model for each separate feature by introducing two1-dimensional Gaussian densi-ties to model the distribution of the examples and counter-examples respectively.They then measure the separation between these two densities(e.g. using the Fisher-distance)to quantify the predic-tive power of the feature under scrutiny and re-move it if necessary.However,compared to a lo-gistic regression model this approach has a number of drawbacks.
Firstly,modeling examples and counter-examples using two Gaussians only works well if these groups are well separated.However,if the examples cluster together and are?anked on both sides by counter-examples,then the estimated Gaussians might have very similar means.As a consequence,the feature will be rejected although it is in fact perfectly acceptable.Secondly,the fact that the two Gaussians are estimated inde-pendently makes the model vulnerable to outliers: the occurrence of a single example among the counter-examples(or vice versa)will in?ate the variance of the corresponding Gaussian,thereby seriously reducing the feature’s ef?cacy.Finally, extensions to higher dimensions become problem-atic,because a2-Gaussian model can certainly not handle a situation in which the counter-examples are distributed in a ring around a central cloud of examples.Multiple Instance Learning The growing body of work on multiple instance learning(MIL)(see [?])aims at tackling the problem of partial rele-vance.Collections of multiple instances(dubbed “bags”)are classi?ed as positive if they contain at least one positive instance,and negative oth-erwise.These instances play a role analogous to the features in CBIR-applications and the MIL-algorithm looks at each feature separately and tries to extract constraints on their values.Lifting these feature-wise constraints to the high-dimensional search space creates hyper-rectangles(APR:axis-parallel rectangles)within which the search is fo-cused.Ideally case these APR’s contain all the examples and none of the counter-examples,but this is exceptional.To handle the more realistic cases of ambiguous data,various cost-functions are introduced to penalize the inclusion of counter-examples(exclusion of examples,respectively). Although these“soft”versions of MIL are simi-lar in spirit to logistic regression,the underlying theoretical model lacks the mathematical simplic-ity and power of the latter.Furthermore,even soft-MIL is based on axis parallel rectangles(i.e.sep-arable models)and can therefore not match the ?exibility of the fully quadratic multi-dimensional regression models that are allowed within our ap-proach.
4.REFERENCES
[1]G Caenen and EJ Pauwels:Regression Models for
Relevance Feedback in CBIR.CWI Tech Report, June2001.
[2]IJ Cox,ML Miller,TP Minka,T Papathomas and
PN Yianilos:The Bayesian Image Retrieval Sys-tem,PicHunter.IEEE Trans.Image Processing, Vol.9(1),Jan2000.
[3]P Hong,Q Tian,T S Huang:Incorporate Sup-
port Vector Machines to Content-Based Image Re-trieval with Relevant Feedback.Proc ICIP2000, v.III,pp.750-753.
[4]O Maron:Learning from Ambiguity.MIT,PhD-
Thesis,1998.
[5]P McCullagh,JA Nelder:Generalized Linear Mod-
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[6]Kinh Thieu and Paul Viola,Boosting Image Re-
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最新文件---------------- 仅供参考--------------------已改成-----------word文本 --------------------- 方便更改 高考英语真题词义辨析 1.What was so ______ about Jasmine Westland’s victory was that she came first in the marathon bare-footed. A. awful B. essential C. impressive D. obvious 2.The old rules have to be ______ because they only applied to the circumstances that existed when they were made a hundred years ago. A. developed B. established C. observed D. revised 3.My good performance in the job interview left me about my future and about what I can do here. A. puzzled B. sensitive C. optimistic D. embarrassed 4.In an attempt to save money, some housewives like to go from shop to shop to search for real _______. A comments B. champions C. bargains D. traditions 5.Her ______ for writing was a desire for women to get the right to higher education. A. motivation B. qualification C. talent D. technique 6.Hardly had Sabrina finished her words when Albert said ______, “Don’t be so mean,” po inting a finger of warning at her. A. dreadfully B. guiltily C. indirectly D. sharply 7.Instead of blaming the child who had broken the vase, she gave him a ______ smile and let him go. A. cautious B. grateful C. tolerant D. wild 8.Legend has it that the origin of the Dragon Boat Festival is to the soul of Qu Yuan. A. remember B. remind C. recover D. recall 9.Top graduates from universities are ______ by major companies. A. chased B. registered C. offered D. compensate
英语语用学教程 English Pragmatics: A Coursebook 1. Background and definitions Warming up activity What does the boy still need to learn about using language? A little boy comes in the front door. Mother: Wipe your feet, please. He removes his muddy shoes and socks and carefully wipes his clean feeton the doormat. 1.1Background of pragmatics 1.1Definitions of pragmatics Pragmatics is the study of how the speaker produces and how the hearerunderstands the peculiar uses of language. 1.2Component vs. perspective分相论与综观论 1.2.1Component view of pragmatics Davis points out in his book Pragmatics: A Reader(1991) that pragmatics,likephonetics,phonology,morphology,syntaxandsemantics,isacomponent of linguistics. 1.2.2Perspective view of pragmatics Verschueren(1999) proposes that pragmatics is not a component oflinguistics, but a new way of looking at language. Pragmaticsisspecifiedas“ageneralcognitive,social,andculturalperspective on linguistic phenomena in relation to their usage in forms ofbehavior”(Verschueren,1999)
Chapter 6 Pragmatics 语用学 1.What is pragmatics? 什么是语用学？ Pragmatics can be defined as the study of how speakers of a language use sentences to effect successful communication. As the process of communication is essentially a process of conveying meaning in a certain context, pragmatics can also be regarded as a kind of meaning study. It places the study of meaning in the context in which language is used. 语用学研究的是说某种语言的人怎样用句子去实现成功的交际。 由于交际的过程从本质来说是在一定的语境中表达意义的过程，因而语用学的本质是一种意义研究。它是一种将语言置于使用的语境中去的意义研究。 2.Pragmatics and semantics 语用学和语义学 Pragmatics and semantics are both linguistic studies of meaning, but they are different. What essentially distinguishes semantics and pragmatics is whether in the study of meaning, the context of use is considered. If it is not considered, the study is restricted to the area of traditional semantics; if it is considered, the study is being carried out in the area of pragmatics. 语用学和语义学都是对意义的语言学研究，但两者是不同的。它们的本质区别在于研究意义时是否考虑了语言使用的语境。没有考虑到语境进行的研究就没有超出传统语义学的研究范围；相反，考虑到语境进行的研究就属于语用学的研究范围。 3.Context 语境 Context is essential to the pragmatic study of language. It is generally considered as constituted by the knowledge shared by the speaker and the hearer. 语境是语言的语用研究中不可缺少的概念。它一般被理解为说话者和听话者所共有的知识。The shared knowledge is of two types: the knowledge of the language they use, and the knowledge about the world, including the general knowledge about the world and the specific knowledge about the situation in which linguistic communication is taking place. 共有的知识包括他们所使用的语言方面的知识和双方对世界的认识，包括对世界的总的认识和对正在进行的语言交际所处的环境的具体认识。 4.Sentence meaning and utterance meaning 句子意义和话语意义The meaning of a sentence is abstract, and de-contextualized, while utterance meaning is concrete, and context-dependent. Utterance is based on sentence meaning; it is the realization of the abstract meaning of a sentence in a real situation of communication, or simply in a context. 句子的意义是抽象的，非语境化的，而话语的意义是具体的，受语境制约的。话语意义基于句子意义；它是一个句子的抽象意义在特定语境中的具体体现，或简而言之，在一个语境中的具体化。 5.Speech act theory 言语行为理论 Speech act theory is an important theory in the pragmatic study of language. It was originated with the British philosopher John Austin in the late 50’s of the 20th century. 言语行为理论是语言语用研究中的一个重要理论。它最初是由英国哲学家约翰.奥斯汀在20世纪50年代提出的。 According to speech act theory, we are performing actions when we are speaking.
常见的英文近义词辨析100组 1、abide,adhere,conform,comply“遵守”。 abide v.后接by表示“遵守，同意”。 I will abide by the director'decision.我将遵从主任的决定。 adhere v.后接to表示“遵守”。(当然adhere一词的其它意思如“坚持；粘附”也经常被考到。) Car drivers must adhere to the rules of driving.汽车司机必须遵守驾驶规则。 conform v.后接to表示“遵守，符合”。 All individuals are required to conform to the laws made by their governments.每个人都应该遵守政府制订的法律。comply v.后接with表示“遵守，服从”，用于正式的场合。 Our company complies with governmental regulations on paying taxes.我们公司遵守政府有关纳税的规定。 2、abnormal,uncommon,disordered“反常的” abnormal a.不正常的，反常的(但并非罕见)，指行为或现象(如气候)的异常。 His body temperature has been abnormal for3days,the highest point reaching40.5degree centigrade.他的体温三天来一直都不正常，最高的时候达到40.5摄氏度。(尽管身体发烧不正常，但生活中也时有发生。) uncommon a.罕见的，不平常的，指很少经历或很少见到的状况；特别的，出色的。 Hurricanes are uncommon in England.飓风在英国非常罕见。 That is uncommon instant coffee;it tastes great!那速溶咖啡质量上乘，味道好极了！ disordered a.混乱的，杂乱的；(精神或身体)有病的。 We couldn't understand her disordered presentation.我们听不明白她条理不清的陈述。 3、abolish,cancel,eliminate,dispose,erase,exclude,extinguish都有“取消，除掉”的意思。 abolish v.指对法律、习俗、制度的废除；完全破坏。 The government abolished the tax on alcohol.政府取消了酒税。 cancel v.对预先安排的某种活动(如旅行、计划、会议等)的取消；删去(字、句)。 The meeting has been cancelled because of the flu.会议由于流感而取消了。 eliminate v.指消除、淘汰已经存在但是现在不需要的东西。 The losing team was eliminated from further competition.失利的那个队被淘汰了，不能参加下一阶段的比赛。 The doctor helped him eliminate toxins from the intestine.医生帮助他排出肠中毒素。 dispose v.处理，处置；表示“除掉、扔掉”时常与of连用。(这一点需要注意。) After your picnic,please dispose of the litter.野餐后请清除掉垃圾。 erase v.除去，擦掉，指有意识地除去字迹、声音等。 I erased the music on the tape before recording on the tape again.我在往磁带上录音之前先消掉了上面的音乐。exclude v.排斥；排除，不包括在内，与include互为反义词。 The restaurant excludes anyone who is not properly dressed from entering.衣冠不整者不得进入该餐馆。extinguish v.熄灭，扑灭(火)；使沉默，使暗淡。 Firefighters extinguished a big fire.消防队员扑灭了大火。 4、abstract,digest,outline,summary“要点，摘要” abstract n.概要，摘要，尤其指对学术论文或法律论据作的简述。 I have read the abstract of his book.我已经读了他书的概要。 digest n.(篇幅较长的)摘要，文摘，它是对原文的浓缩而不是对原文的简单解释，浓缩后仍保持原文的顺序、重点和风格。 Reader's Digest《读者文摘》 outline n.要点，大纲，概要。 She made an outline of ideas she wanted to present in her talk.她把自己要谈的想法列了一个提纲。 summary n.总结，摘要，指用寥寥数语概括文章或者讲话的要点，不考虑原文的风格。 5、absurd,ridiculous,silly都有“愚蠢，可笑”的意思。 absurd a.荒谬的，可笑的，指因不符合常识、违反真理或不合逻辑而令人发笑。 There was an absurd idea that the earth was flat and motionless.过去曾经有一种荒谬的观点认为地球呈扁平状而且静止不动。 ridiculous a.荒唐可笑的，指因为愚昧无知而令人发笑并成为笑柄，含有蔑视成分。 It is ridiculous to judge a foreign culture only by its food.仅仅根据一个国家的饮食来评价该国家的文化是荒唐可笑的。silly a.愚蠢的，傻的，指由于单纯或者头脑简单而显得愚蠢。 a silly little boy傻小子
高中英语名词经典习题(含答案) 一、单项选择名词 1．To be honest, she had a great ______ for the small town where she grew up. A．effect B．effort C．affection D．patience 【答案】C 【解析】 【详解】 考查名词词义辨析。句意：说实话，她对她长大的那个小镇有很深的感情。A. effect影响；B. effort努力；C. affection深情；D. patience耐心。短语have a great affection for sth. 意为“对某物钟爱，对某物有深挚的感情”。故选C。 2．An advantage of living on the top floor of a high rise building is that you can get a good _________. A．scene B．scenery C．sight D．view 【答案】D 【解析】 【详解】 考查名词辨析。句意：住在高楼大厦顶层的优点是你能看很好地欣赏风景。A. scene 情景B. scenery景色 C. sight 景象 D. view风景。view“风景”普遍用语,指从某个角度所看到的风景。根据句意，故D选项正确。 【点睛】 Scene:1. 某地的景色、风景，指展现在眼前的情景，也可以指scenery的一部分 3．Only when women are enabled to become strong will our families, our economies, and our societies reach their fullest . A．distinction B．innovation C．potential D．appreciation 【答案】C 【解析】 【详解】 考查名词。A. distinction区别； B. innovation创新； C. potential潜能； D. appreciation 欣赏。句意：只有当妇女能够变得强大时，我们的家庭、经济和社会才能充分发挥其潜力。结合句意可知答案为C。 4．The girl received no approval of his family as her marriage with an African boy was regarded as a ____ from tradition. A．violation B．departure
初中英语同义词辨析 英语学习过程中经常遇到同义、近义词的辨析，这是英语学习的难点之一，也是中考考点之一，现在把自己整理的一些初中常见的同义近义词的辨析放在这里，和朋友们交流，也供学生朋友选择学习。1、talk tell speak say speak 和talk 通常用作不及物动词，都有“说话”之意。在会议上发言用speak，名词为speech； 随便漫谈用talk，其名词还是talk； tell表示“讲述”或“告诉”； say表示“说”；例如： can we speak about plans for the holidays? 我们谈谈假期的打算好吗？ the patient is too weak to speak. 病人太衰弱了，不能说话。 my father was talking with my teacher when i got home yesterday。昨天我到家时我父亲正在和我的老师谈话。 i always tell my daughter a story before she goes to sleep. 女儿睡觉之前，我总讲故事给她听。 it‘s impossible to tell who will win the next election. 下届选举谁能获胜无法预知。 she said nothing to me about it.
关于这一点，她什么也没有对我讲。 *speak 当及物动词用时，宾语一般是语言或词语之类的词。如: does anyone speak english here? 这儿有人会说英语吗？ 2、good well nice good 形容词，好的，合适的，新鲜的，擅长的。 well 作形容词时，指"(身体）健康的”；还可用作副词，修饰动词。nice形容词，美好的，令人愉快的，可爱的，特指取悦感官的事物。she is good at english.她擅长英语。 this cake tastes good.这蛋糕好吃。 his mother is very well.他妈妈很健康。 she is a nice little girl.她是个可爱的小姑娘。 3、look see watch read 看 look通常为不及物动词，强调“看”的动作，指“认真看”，强调看的对象时，后须接介词at才能带宾语，即look at。 see是及物动词侧重于看的结果“看见，看到” watch 作动词，意为“观看，注视”，多指观看运动着的事物，如电视、比赛、表演等。 read 主要强调“读，阅读，朗读”，汉语中常译成“看”，多指看书、报、杂志。 look！the man is coming！看！那个男的来了。 look at the map .看这张地图。 can you see the dog over there？你能看见那儿的狗吗？
高考英语语法复习动词词义辨析 动词是是各类考试的重点，高考试题中，单项填空、完形填空和改错等三项题型中，动词辨义的比重较大，并逐年增加。动词辨义主要指：1、形状相同的动词之间辨义。如：lie, lay; hanged, hung; rise, raise; sit, seat等。2、意义相近的动词之间辨义。如：borrow, lend; speak, say, talk; hope, wish等。3、动词与其它词形相近、意义相似的词的辨义。如：advise, advice; cost, worth; pass, past 等。4、意义不同，但容易混淆的动词的辨义。如：explain, say; discover, invent, uncover; find, find out等。5、某些常用动词的习惯用法的辨义。如：ask, give, call, make, find, get, keep, want, see, hear等。6、某些常用动词短语的辨义。如：give in, give up, turn on, turn off, turn down, turn up等。 （一）易混动词 1 2、rise和raise：rise是不及物动词，其过去式是rose，过去分词是risen，而raise是及物动词，是规则动词。 3、hear与listen to：hear侧重点是听到，听见什么，而listen to是侧重于听的倾向，但hear用于无意中的听见，而listen to却用于集中注意力的听。 4、see, watch和look：see用作看电影，剧目；watch则用作看电视比赛，而watch还有在旁观看之意。如：Are you going to play or only watch?；look一般用作不及物动词，只是当盯着某人看时用作及物动词，如：The little boy looked me in the face.（小男孩直盯着我的脸。） 5、wind和wound：wind意为蜿蜒而行，其过去式与过去分词都是wound，而动词原形wound 意为伤害，其过去式、过去分词都是wounded。 6、hang的用法：hang有两个意思：一为悬挂，是不规则动词，过去式、过去分词都是hung；二为绞刑，是规则动词，其过去式、过去分词都是hanged。 7、hear的过去分词born与borne：bear作为出生讲有两个过去分词born，borne。只有当be+born…短语后没有by介词短语时，才可用born。如:He was born in Shanghai. 而作它用时要用borne。如：She has borne five children. 但如果作忍受讲，则一律用borne。 8、sit与seat：seat为及物动词时是作容纳讲，sit只是表示一动作。seat如果表示就座时要用be seated。如：They were seated at their desks. 或用seat oneself, 比如:I seated myself in the armchair. 9、borrow, lend与keep：借入英文中用borrow，借出用lend，但这两个词都是截止性动词或瞬间动词，不能用于长时间的动作，所以我能借多久应用keep。 10、win与beat：win作胜、赢讲时其后应接，a game, an argument, a battle, a prize, a contest, a race, a bet，但不能接人，如果接人则有另外的含意。如:I have won him. 即我已说服他了，我赢得他的好感。而beat是及物动词为击败、胜过讲，直接接人、队。 11、steal与rob：steal为偷。rob为抢，其用法不同。steal其后接物+from+某人、某地，而rob 其后接人+of+抢的物品。 12、fit与suit：fit与suit均可作合适讲，但英文中却用在不同的地方。如fit用于尺寸大小的合适，而suit则多用于颜色式样的合适。 13、take, bring 与fetch：英文中拿三个词，即拿来，拿去，去取然后回来(即双程)。所以拿来，带来是bring，拿去带走是take，而去取回来是fetch。 14、shut与close：shut与close有时是可以互换的，但有些地方则不可这样做。如:在正式场合多用close，而在命令，态度粗暴的场合则用shut。如:Shut your mouth!(闭嘴)；又如:Shut up. 在指铁路、公路交通关闭或停止使用的场合，则要用close。 15、answer与reply：作为回答讲answer是及物动词，如作不及物动词，则意义不同，如answer for，意为向某人或向某事负责。而reply作回答讲是不及物动词，后跟宾语时，要加上to。
Chapter 6 pragmatics 语用学 知识点： 1.*Definition: pragmatics; context 2.*sentence meaning vs utterance meaning 3.*Austin’s model of speech act theory 4.Searle’s classification of speech acts 5.*Grice’s Cooperative Principle 考核目标： 识记：*Definition: pragmatics; context 领会：Searle’s classification of speech acts 综合应用：sentence meaning vs utterance meaning；Austin’s model of speech act theory；Grice’s Cooperative Principle 一、定义 1. Pragmatics语用学: Pragmatics: the study of how speakers of a language use sentences to effect successful communication. Pragmatic can also be regarded as a kind of meaning study.语用学研究的是语言使用者是如何使用句子成功进行交际的。语用学也可以看作是一中意义研究。(它不是孤立地去研究语义，而是把语义置于使用语境中去研究的一门学科。) 2. Context 语境：The notion of context is essential to the pragmatic study of language, it’s generally considered as constituted by the knowledge shared by the speaker and the hearer. 语境这个概念对语言的语用研究来说是必不可少的。一般认为他是由言者和听者的共享知识所构成的。 二、知识点 6.1.2 pragmatics vs. semantics语用学与语义学 二十世纪初，Saussure’s Course in General Linguistics 一书的出版标志着现代语言学研究的开始，同时也为现代语言学奠定了基础调，即语言应该作为一个独立的，内在的系统来加以研究。 语用学和语义学既有相关性又有相异性。两者都是对意义的研究。传统语义学把语义看成是抽象的，内在的，是语言本身的特性，不受语境的影响。因此传统语义学只研究语义的内在特征，不把语义研究置于语境中来考察。语用学研究的是交际过程中语言意义的表达和理解。语用学家认为不把意义放在语境中来考虑就不可能对语义进行充分的描述，因此在研究语义时是否考虑语境便成了传统语义学和语用学的根本区别所在。 Semantics 和Pragmatics的区分 Pragmatics studies how meaning is conveyed in the process of communication. The basic difference between them is that pragmatics considers meaning in context, traditional semantics studies meaning in isolation from the context of use.
２０１０年第２期（总第７４期） 边疆经济与文化 ＴＨＥＢＯＲＤＥＲＥＣＯＮＯＭＹＡＮＤＣＵＬｒｌｌＪＲＥ Ｎｏ．２．２０ｌＯ ＧｅｎｅｒａｌＮｏ．７４ 【教育纵横】 语用学对英语教学的作用 于松 （哈尔滨师范大学西语学院，哈尔滨１５００８０） 摘要：长期以来，中国英语教学都采取传统模式，教师在教学过程中过分注重学生词汇、语法以及句子分析的能力，造成了学生的满腹理论而不知如何应用的结果。语用学作为一门新兴学科，主要研究语言的应用并已被应用到众多领域。在英语教学中结合语用学的理论不但可以教会学生学会基本的语言知识，更可以促使其掌握语用知识，正确的使用英语这一交际工具。 关键词：语用学；语用学理论；英语教学；教学启示 中图分类号：Ｇ４２４．１文献标志码：Ａ文章编号：１６７２－５４０９（２０１０）０２－０１５６－０２ 一、引言 当代英语教学大都将英语视为一种语言知识进行传授，只注重孤立地对学生进行英语单词、语法、句法、翻译、写作等方面的教学，却忽视了对学生实际运用英语语言知识的能力的培养。通过以上教学，学生可以在应试中取得很好的成绩，然而在用语言进行交流时却不能很好的运用所学知识进行有效的交流。语言是交际的工具，语言各方面的知识是相互联系的整体，只有掌握了具体的语言形式所具有的功能并结合一定的语境，才能真正掌握和使用一门语言，因此在英语教学中不仅应重视对英语听说读写等基础知识的教学，更应注重语用方面的教学，这是本文研究的目的。 二、语用学对英语教学的作用 英语教学实际上是一个动态的过程。是学生通过书面阅读等方式进行信息交流、理解，并在理解的过程中增长知识、提高能力的过程。因此英语教师应充分认识到英语教学并不仅仅是教授语音、词汇、语法等语言基础知识，还应对语言环境、语言知识、文化背景等进行语用分析。在实际教学中不仅要明确提出提高学生语用能力的教学目标，在实际教学的各个环节中都要贯彻执行这一目标。 １．语用学对词汇教学的作用 传统英语词汇教学往往注重词汇本身的意义和语法意义，孤立地讲词义而不是将词汇放人具体语境去教词义，然而在不同的语境中同一个词也会有不同的意义。学生在学习过程中能很好的记住某一单词的各种意义，但到了实际应用时却一头雾水，常常出现语用错误。由于同一单词在不同语境中会有不同的意义，学生在阅读文本时只知道该词的基本意义，因此在理解文本时产生障碍，不能有效的进行认知。例如ｇｏｏｄ一词，简单的定义为“好的”是不合适的，例如：“ｈｅｉｓａｇｏｏｄｂｏｙ”并不一定表示“他是个好男孩”。如果在前面加上“ｈｅｈａｓｓｔｏｌｅｎ１０，０００ｂｉｃｙｃｌｅｓ．”则表示“他是个坏男孩。”此外，在词汇教学过程中，教师还应让学生注意到某些词汇用法的局限。 例如，ｃｏｎｆｅｒｅｎｃｅ一词简单的定义为会议，但它往往不能与我们较熟悉的ｍｅｅｔｉｎｇ互换。原因是前者用法较正式通常指较大型的会议。后者指一般的、较小型的、常见的会议。例如我们在日常生活中常用“ｔＩｌｅｔｅａｃｈｅｒａｓｋｅｄＭａｒｙｔｏａｔｔｅｎｄｔｈｅｃＩａｓｓ．ｒｏｏｍｍｅｅｔｉｎｇａｔ１２０’ｃｌｏｃｋ．”而不是用“ｔｈｅｔｅａｃｈ．ｅｒａｓｋｅｄＭａｒｙｔｏａｔｔｅｎｄｔｈｅｃｌａｓｓｒｏｏｍｃｏｎｆｅｒｅｎｃｅ．” ２．语用学与语法教学 语法教学在传统的英语教学中也同样采用孤立的教学方式。语法教学的常规模式通常是教师以一本语法书为基础，在课堂上尽可能多的向学生罗列语法知识，而学生在课堂上主要的任务就是不停地记笔记，课后花费大力气记住老师所讲的语法要点。这种教学方法的后果往往是学生有着很好的理论基础，可是实际应用时却错误百出。另一种结果是学生为了应付考试进行大量的练习，考试时能够取得不错的成绩，可考完试后所学的语法知识便忘在脑后。教授语法知识的目的是为了让学生掌握语用能力，掌握语法知识和句型是正确运用英语的前提，但有了这一前提并不代表有了语用能力，如果把语法教学与语用知识结合起来无疑会收到更好的 收稿日期：２００９－０９．１５ 作者简介：于松（１９８５一），女，黑龙江尚志人，硕士研究生，从事外国语言学及应用语言学研究。围删一舢Ｊｗ哪蝴 万方数据
英语常用同义词辨析 H habit, custom, manners, usage, practice, convention, tradition这些名词均含有“习惯，风俗，传统”之意。habit〓侧重于自然养成的，不易去掉的个人习惯。 custom〓指一个国家、一个民族、一个地区或一个社会的习惯、行为方式或风俗习惯。也可指个人的习惯。manners〓指在某一时期或某一团体中盛行的社会礼仪或社会风俗模式。 usage〓指持续时间长，为很多人公认采纳的惯例、习俗。 practice〓常指习惯性的做法或行为。 convention〓多指约定俗成的行为方式规则或公认的标准，或指艺术上公认的技巧。 tradition〓广义指世代相传的伦理、行为规范和生活方式，特指因长久奉行而几乎具有法律效力的习惯、习俗，侧重历史意义。 hang, suspend这两个动词均含“挂，吊”之意。 hang〓通常指一端固定于一点或一条线上，另一端悬于空中的状态。 suspend〓侧重悬挂物支撑点的灵活性和活动自由。 happen, occur, chance, take place这些词语都可表示“发生”之意。 happen〓普通用词，泛指一切客观事物或情况的发生，强调动作的偶然性。 occur〓较正式用词，可指意外地发生，也可指意料中的发生。 chance〓侧重事前无安排或无准备而发生的事，特指巧合。 take place〓多指通过人为安排的发生。 happy, glad, cheerful, joyful, merry, delightful, gay, pleasant, nice, jolly, agreeable这些形容词均含“愉快的，高兴的”之意。 happy〓侧重感到满足、幸福或高兴。 glad〓最普通用词，语气较弱，表示礼貌的惯用语。指乐于做某事或因某事而感到满足，常表愉快的心情。 cheerful〓多指因内心的愉快而表现出兴高采烈，是强调而自然的感情流露。 joyful〓语气较强，强调心情或感情上的欣喜。 merry〓指精神情绪的暂时高涨，表示欢乐、愉快的心境或情景，侧重充满欢笑声和乐趣。 delightful〓指能带来强烈的快乐，激起愉快的情感，用于非常愉快的场合。 gay〓侧重无忧无虑、精神昂扬、充满生命的快乐。 pleasant〓侧重给人以“赏心悦目”或“愉快的，宜人的”感受。 nice〓语气较温和，泛指任何愉快或满意的感觉。 jolly〓通俗用词，多指充满快乐与喜悦的神情。 agreeable〓指与感受者的愿望、情趣或受好等和谐一致而带来的心情上的快意。
高三英语词义辨析和练习 afford 熟义：v 买得起；支付得起 例句：I couldn’t afford the rent on my own. 我单靠自己付不起房租。 生义：v 抽得出（时间） 例句：She couldn’t afford the time off work to see him. 她工作太忙，抽不出时间去看他。 against 熟义：prep 反对 例句：Are you for or against my proposal? 你赞成还是反对我的提议？ 生义：prep 以……为背景；以防 例句：His red clothes stood out clearly against the snow. 他的红色衣服在白雪中格外显眼。 We are insured the car against fire, theft and accident. 我们替车买了火险、盗窃险和交通意外险。 air 熟义：n 空气 例句：Let’s go out for some fresh air. 咱们出去呼吸点新鲜
空气。 生义：v 公开发表（看法） 例句：Staff will get a chance to ask questions and air their views. 员工将有机会提问并发表意见。 business 熟义：n 商业；买卖；生意 例句：She works in the computer business. 她从事电脑业。生义：n 职责 例句：It is the business of the police to protect the community. 警察的职责是保护社会。 coin 熟义：n 硬币 例句：a ten-cent coin 面值十分的硬币 生义：v 创造（新词语） 例句：Jaron Lanier coined the term “virtual reality”and pioneered its early development. 加隆·雷尼尔首创“虚拟现实”一词，并率先进行早期开发。 collect 熟义：v 收集；收藏
1.Pragmatics is the study of language in use. Pragmatics is concerned with the study of meaning as communicated by a speaker (or writer) and interpreted by a listener (or reader). Pragmatics is the study of speaker meaning Pragmatics is the study of contextual meaning Pragmatics is the study of the expression of relative distance. Pragmatics is the study of the relationships between linguistic forms and the users of those forms. 2.Syntax is the study of the relationships between linguistic forms, how they are arranged in sequence, and which sequences are well-formed. 3.Semantics is the study of the relationships between linguistic forms and entities in the world; that is, how words literally connect to things. 4.Deixis 指示语is a technical term (from Greek) for one of the most basic things we do with utterances. It means ‘pointing’ via language.Any linguistic form used to accomplish this ‘pointing’ is called a deictic expression. Deictic expressions are also sometimes called indexicals. They are among the first forms to be spoken by very young children and can be used to indicate people via person deixis(such as, ‘me’, ‘you’), or location via spatial deixis(such as ‘here’, ‘there’), or time via temporal deixi s (such as ‘now’, ‘then’). 5.Proximal terms近指are typically interpreted in terms of the speaker’s location, or the deictic center指示中心.‘this’, ‘there’, ‘now’, ‘then’near speaker 6.Distal terms远指can simply indicate ‘away’ from speaker’, but, in some languages, can be used to distinguish between ‘near addres see’ and ‘away from both speaker and addressee’. 7.Person deixis人称指示语clearly operates on a basic three-part division, exemplified例证by the pronouns for first person, second person, and third person./ forms used to point to people, “me””you” 8.Expressions which indicate addressee higher status are described as honorifics敬语. 9.The discussion of the circumstances which lead to the choice of one of these forms rather than another is sometimes described as social deixis./forms used to indicate relative social status 10.A distinction between forms used for familiar versus a non-familiar addressee in some languages. This is known as the T/V distinction. 用复数形态来表示单数敬语，在语言中叫T-V distinction。此概念由1960 年的学者Brown 和Gilman 提出，他们将第二人称单数分为两种形态：T 形态（T-form）和V 形态（V-form），前者在非正式场合、尊称呼卑、关系亲密的人之间使用，后者在正式场合、下级称呼上级、称呼陌生人的时候使用 11.exclusive ‘we’ (speaker plus other(s), excluding addressee)； inclusive ‘we’ (speaker and addressee included). 12.spatial deixis空间指示语- the relative location of people and things is being indicated. Eg, here, there/ forms used to point to location. 13.‘Yonder’那边(more distant from speaker) ‘hither’这边(to this place) ‘thence’从那里(from that place) 14.deictic projection指示投射manipulate speaker’s location eg: I am not here now./speakers acting as if they are somewhere else. 15.psychological distance心理距离I don’t like that. it is ‘invested’ with meaning in a context by a speaker./speaker’s marking of how close or distant something is perceived感知to be. 16.temporal deixis时间指示Back in an hour. the coming week./ forms used to point to location in time 17.It is clear that the present tense is the proximal form近端形式and the past tense is the distal form远端形式. if-clauses 18.In temporal deixis, the remote or distal form can be used to communicate not only distant from current time, but also distant from current reality or facts. 19.Discourse deixis/ textual deixis语篇指示语“the use of expressions within some utterance to refer to some portion 部分of the discourse that contains that utterance (i ncluding the utterance itself)”This is what he did to me. He ripped 撕扯my shirt and hit me on the nose