Longman Defining Vocabulary

Multinomial Randomness Models for Retrieval withDocument FieldsVassilis Plachouras1and Iadh Ounis21Yahoo!Research,Barcelona,Spain2University of Glasgow,Glasgow,UKvassilis@,ounis@ Abstract.Documentfields,such as the title or the headings of a document,offer a way to consider the structure of documents for retrieval.Most of the pro-posed approaches in the literature employ either a linear combination of scoresassigned to differentfields,or a linear combination of frequencies in the termfrequency normalisation component.In the context of the Divergence From Ran-domness framework,we have a sound opportunity to integrate documentfieldsin the probabilistic randomness model.This paper introduces novel probabilis-tic models for incorporatingfields in the retrieval process using a multinomialrandomness model and its information theoretic approximation.The evaluationresults from experiments conducted with a standard TREC Web test collectionshow that the proposed models perform as well as a state-of-the-artfield-basedweighting model,while at the same time,they are theoretically founded and moreextensible than currentfield-based models.1IntroductionDocumentfields provide a way to incorporate the structure of a document in Information Retrieval(IR)models.In the context of HTML documents,the documentfields may correspond to the contents of particular HTML tags,such as the title,or the heading tags.The anchor text of the incoming hyperlinks can also be seen as a documentfield. In the case of email documents,thefields may correspond to the contents of the email’s subject,date,or to the email address of the sender[9].It has been shown that using documentfields for Web retrieval improves the retrieval effectiveness[17,7].The text and the distribution of terms in a particularfield depend on the function of thatfield.For example,the titlefield provides a concise and short description for the whole document,and terms are likely to appear once or twice in a given title[6].The anchor textfield also provides a concise description of the document,but the number of terms depends on the number of incoming hyperlinks of the document.In addition, anchor texts are not always written by the author of a document,and hence,they may enrich the document representation with alternative terms.The combination of evidence from the differentfields in a retrieval model requires special attention.Robertson et al.[14]pointed out that the linear combination of scores, which has been the approach mostly used for the combination offields,is difficult to interpret due to the non-linear relation between the assigned scores and the term frequencies in each of thefields.Hawking et al.[5]showed that the term frequency G.Amati,C.Carpineto,and G.Romano(Eds.):ECIR2007,LNCS4425,pp.28–39,2007.c Springer-Verlag Berlin Heidelberg2007Multinomial Randomness Models for Retrieval with Document Fields 29normalisation applied to each field depends on the nature of the corresponding field.Zaragoza et al.[17]introduced a field-based version of BM25,called BM25F,which applies term frequency normalisation and weighting of the fields independently.Mac-donald et al.[7]also introduced normalisation 2F in the Divergence From Randomness (DFR)framework [1]for performing independent term frequency normalisation and weighting of fields.In both cases of BM25F and the DFR models that employ normali-sation 2F,there is the assumption that the occurrences of terms in the fields follow the same distribution,because the combination of fields takes place in the term frequency normalisation component,and not in the probabilistic weighting model.In this work,we introduce weighting models,where the combination of evidence from the different fields does not take place in the term frequency normalisation part of the model,but instead,it constitutes an integral part of the probabilistic randomness model.We propose two DFR weighting models that combine the evidence from the different fields using a multinomial distribution,and its information theoretic approx-imation.We evaluate the performance of the introduced weighting models using the standard .Gov TREC Web test collection.We show that the models perform as well as the state-of-the-art model field-based PL2F,while at the same time,they employ a theoretically founded and more extensible combination of evidence from fields.The remainder of this paper is structured as follows.Section 2provides a description of the DFR framework,as well as the related field-based weighting models.Section 3introduces the proposed multinomial DFR weighting models.Section 4presents the evaluation of the proposed weighting models with a standard Web test collection.Sec-tions 5and 6close the paper with a discussion related to the proposed models and the obtained results,and some concluding remarks drawn from this work,respectively.2Divergence from Randomness Framework and Document Fields The Divergence From Randomness (DFR)framework [1]generates a family of prob-abilistic weighting models for IR.It provides a great extent of flexibility in the sense that the generated models are modular,allowing for the evaluation of new assumptions in a principled way.The remainder of this section provides a description of the DFR framework (Section 2.1),as well as a brief description of the combination of evidence from different document fields in the context of the DFR framework (Section 2.2).2.1DFR ModelsThe weighting models of the Divergence From Randomness framework are based on combinations of three components:a randomness model RM ;an information gain model GM ;and a term frequency normalisation model.Given a collection D of documents,the randomness model RM estimates the probability P RM (t ∈d |D )of having tf occurrences of a term t in a document d ,and the importance of t in d corresponds to the informative content −log 2(P RM (t ∈d |D )).Assuming that the sampling of terms corresponds to a sequence of independent Bernoulli trials,the randomness model RM is the binomial distribution:P B (t ∈d |D )= T F tfp tf (1−p )T F −tf (1)30V .Plachouras and I.Ouniswhere TF is the frequency of t in the collection D ,p =1N is a uniform prior probabilitythat the term t appears in the document d ,and N is the number of documents in the collection D .A limiting form of the binomial distribution is the Poisson distribution P :P B (t ∈d |D )≈P P (t ∈d |D )=λtf tf !e −λwhere λ=T F ·p =T FN (2)The information gain model GM estimates the informative content 1−P risk of the probability P risk that a term t is a good descriptor for a document.When a term t appears many times in a document,then there is very low risk in assuming that t describes the document.The information gain,however,from any future occurrences of t in d is lower.For example,the term ‘evaluation’is likely to have a high frequency in a document about the evaluation of IR systems.After the first few occurrences of the term,however,each additional occurrence of the term ‘evaluation’provides a diminishing additional amount of information.One model to compute the probability P risk is the Laplace after-effect model:P risk =tf tf +1(3)P risk estimates the probability of having one more occurrence of a term in a document,after having seen tf occurrences already.The third component of the DFR framework is the term frequency normalisation model,which adjusts the frequency tf of the term t in d ,given the length l of d and the average document length l in D .Normalisation 2assumes a decreasing density function of the normalised term frequency with respect to the document length l .The normalised term frequency tfn is given as follows:tfn =tf ·log 2(1+c ·l l )(4)where c is a hyperparameter,i.e.a tunable parameter.Normalisation 2is employed in the framework by replacing tf in Equations (2)and (3)with tfn .The relevance score w d,q of a document d for a query q is given by:w d,q =t ∈qqtw ·w d,t where w d,t =(1−P risk )·(−log 2P RM )(5)where w d,t is the weight of the term t in document d ,qtw =qtf qtf max ,qtf is the frequency of t in the query q ,and qtf max is the maximum qtf in q .If P RM is estimatedusing the Poisson randomness model,P risk is estimated using the Laplace after-effect model,and tfn is computed according to normalisation 2,then the resulting weight-ing model is denotedby PL2.The factorial is approximated using Stirling’s formula:tf !=√2π·tftf +0.5e −tf .The DFR framework generates a wide range of weighting models by using different randomness models,information gain models,or term frequency normalisation models.For example,the next section describes how normalisation 2is extended to handle the normalisation and weighting of term frequencies for different document fields.Multinomial Randomness Models for Retrieval with Document Fields31 2.2DFR Models for Document FieldsThe DFR framework has been extended to handle multiple documentfields,and to apply per-field term frequency normalisation and weighting.This is achieved by ex-tending normalisation2,and introducing normalisation2F[7],which is explained below.Suppose that a document has kfields.Each occurrence of a term can be assigned to exactly onefield.The frequency tf i of term t in the i-thfield is normalised and weighted independently of the otherfields.Then,the normalised and weighted term frequencies are combined into one pseudo-frequency tfn2F:tfn2F=ki=1w i·tf i log21+c i·l il i(6)where w i is the relative importance or weight of the i-thfield,tf i is the frequency of t in the i-thfield of document d,l i is the length of the i-thfield in d,l i is the average length of the i-thfield in the collection D,and c i is a hyperparameter for the i-thfield.The above formula corresponds to normalisation2F.The weighting model PL2F corresponds to PL2using tfn2F as given in Equation(6).The well-known BM25 weighting model has also been extended in a similar way to BM25F[17].3Multinomial Randomness ModelsThis section introduces DFR models which,instead of extending the term frequency normalisation component,as described in the previous section,use documentfields as part of the randomness model.While the weighting model PL2F has been shown to perform particularly well[7,8],the documentfields are not an integral part of the ran-domness weighting model.Indeed,the combination of evidence from the differentfields takes place as a linear combination of normalised frequencies in the term frequency nor-malisation component.This implies that the term frequencies are drawn from the same distribution,even though the nature of eachfield may be different.We propose two weighting models,which,instead of assuming that term frequen-cies infields are drawn from the same distribution,use multinomial distributions to incorporate documentfields in a theoretically driven way.Thefirst one is based on the multinomial distribution(Section3.1),and the second one is based on an information theoretic approximation of the multinomial distribution(Section3.2).3.1Multinomial DistributionWe employ the multinomial distribution to compute the probability that a term appears a given number of times in each of thefields of a document.The formula of the weighting model is derived as follows.Suppose that a document d has kfields.The probability that a term occurs tf i times in the i-thfield f i,is given as follows:P M(t∈d|D)=T Ftf1tf2...tf k tfp tf11p tf22...p tf kkp tf (7)32V .Plachouras and I.OunisIn the above equation,T F is the frequency of term t in the collection,p i =1k ·N is the prior probability that a term occurs in a particular field of document d ,and N is the number of documents in the collection D .The frequency tf =T F − ki =1tf i cor-responds to the number of occurrences of t in other documents than d .The probability p =1−k 1k ·N =N −1N corresponds to the probability that t does not appear in any of the fields of d .The DFR weighting model is generated using the multinomial distribution from Equation (7)as a randomness model,the Laplace after-effect from Equation (3),and replacing tf i with the normalised term frequency tfn i ,obtained by applying normal-isation 2from Equation (4).The relevance score of a document d for a query q is computed as follows:w d,q = t ∈q qtw ·w d,t = t ∈qqtw ·(1−P risk )· −log 2(P M (t ∈d |D )=t ∈q qtw k i =1tfn i +1· −log 2(T F !)+k i =1 log 2(tfn i !)−tfn i log 2(p i ) +log 2(tfn !)−tfn log 2(p ) (8)where qtw is the weight of a term t in query q ,tfn =T F − k i =1tfn i ,tfn i =tf i ·log 2(1+c i ·li l i )for the i -th field,and c i is the hyperparameter of normalisation 2for the i -th field.The weighting model introduced in the above equation is denoted by ML2,where M stands for the multinomial randomness model,L stands for the Laplace after-effect model,and 2stands for normalisation 2.Before continuing,it is interesting to note two issues related to the introduced weight-ing model ML2,namely setting the relative importance,or weight,of fields in the do-cument representation,and the computation of factorials.Weights of fields.In Equation (8),there are two different ways to incorporate weights for the fields of documents.The first one is to multiply each of the normalised term frequencies tfn i with a constant w i ,in a similar way to normalisation 2F (see Equa-tion (6)):tfn i :=w i ·tfn i .The second way is to adjust the prior probabilities p i of fields,in order to increase the scores assigned to terms occurring in fields with low prior probabilities:p i :=p i w i .Indeed,the assigned score to a query term occurring in a field with low probability is high,due to the factor −tfn i log 2(p i )in Equation (8).Computing factorials.As mentioned in Section 2.1,the factorial in the weighting model PL2is approximated using Stirling’s formula.A different method to approximate the factorial is to use the approximation of Lanczos to the Γfunction [12,p.213],which has a lower approximation error than Stirling’s formula.Indeed,preliminary experi-mentation with ML2has shown that using Stirling’s formula affects the performance of the weighting model,due to the accumulation of the approximation error from com-puting the factorial k +2times (k is the number of fields).This is not the case for the Poisson-based weighting models PL2and PL2F,where there is only one factorial com-putation for each query term (see Equation (2)).Hence,the computation of factorials in Equation (8)is performed using the approximation of Lanczos to the Γfunction.Multinomial Randomness Models for Retrieval with Document Fields33 3.2Approximation to the Multinomial DistributionThe DFR framework generates different models by replacing the binomial randomness model with its limiting forms,such as the Poisson randomness model.In this section, we introduce a new weighting model by replacing the multinomial randomness model in ML2with the following information theoretic approximation[13]:T F!tf1!tf2!···tf k!tf !p1tf1p2tf2···p k tf k p tf ≈1√2πT F k2−T F·Dtf iT F,p ip t1p t2···p tk p t(9)Dtf iT F,p icorresponds to the information theoretic divergence of the probability p ti=tf iT Fthat a term occurs in afield,from the prior probability p i of thefield:D tfiT F,p i=ki=1tfiT Flog2tf iT F·p i+tfT Flog2tfT F·p(10)where tf =T F− ki=1tf i.Hence,the multinomial randomness model M in theweighting model ML2can be replaced by its approximation from Equation(9):w d,q=t∈q qtw·k2log2(2πT F)ki=1tfn i+1·ki=1tfn i log2tfn i/T Fp i+12log2tfn iT F+tfn log2tfn /T Fp+12log2tfnT F(11)The above model is denoted by M D L2.The definitions of the variables involved in theabove equation have been introduced in Section3.1.It should be noted that the information theoretic divergence Dtf iT F,p iis definedonly when tf i>0for1≤i≤k.In other words,Dtf iT F,p iis defined only whenthere is at least one occurrence of a query term in all thefields.This is not always the case,because a Web document may contain all the query terms in its body,but it may contain only some of the query terms in its title.To overcome this issue,the weight of a query term t in a document is computed by considering only thefields in which the term t appears.The weights of differentfields can be defined in the same way as in the case of the weighting model ML2,as described in Section3.1.In more detail,the weighting of fields can be achieved by either multiplying the frequency of a term in afield by a constant,or by adjusting the prior probability of the correspondingfield.An advantage of the weighting model M D L2is that,because it approximates the multinomial distribution,there is no need to compute factorials.Hence,it is likely to provide a sufficiently accurate approximation to the multinomial distribution,and it may lead to improved retrieval effectiveness compared to ML2,due to the lower accu-mulated numerical errors.The experimental results in Section4.2will indeed confirm this advantage of M D L2.34V.Plachouras and I.Ounis4Experimental EvaluationIn this section,we evaluate the proposed multinomial DFR models ML2and M D L2, and compare their performance to that of PL2F,which has been shown to be particu-larly effective[7,8].A comparison of the retrieval effectiveness of PL2F and BM25F has shown that the two models perform equally well on various search tasks and test collections[11],including those employed in this work.Hence,we experiment only with the multinomial models and PL2F.Section4.1describes the experimental setting, and Section4.2presents the evaluation results.4.1Experimental SettingThe evaluation of the proposed models is conducted with TREC Web test collection,a crawl of approximately1.25million documents from domain.The .Gov collection has been used in the TREC Web tracks between2002and2004[2,3,4]. In this work,we employ the tasks from the Web tracks of TREC2003and2004,because they include both informational tasks,such as the topic distillation(td2003and td2004, respectively),as well as navigational tasks,such as named pagefinding(np2003and np2004,respectively)and home pagefinding(hp2003and hp2004,respectively).More specifically,we train and test for each type of task independently,in order to get insight on the performance of the proposed models[15].We employ each of the tasks from the TREC2003Web track for training the hyperparameters of the proposed models.Then, we evaluate the models on the corresponding tasks from the TREC2004Web track.In the reported set of experiments,we employ k=3documentfields:the contents of the<BODY>tag of Web documents(b),the anchor text associated with incoming hyperlinks(a),and the contents of the<TITLE>tag(t).Morefields can be defined for other types offields,such as the contents of the heading tags<H1>for example. It has been shown,however,that the body,title and anchor textfields are particularly effective for the considered search tasks[11].The collection of documents is indexed after removing stopwords and applying Porter’s stemming algorithm.We perform the experiments in this work using the Terrier IR platform[10].The proposed models ML2and M D L2,as well as PL2F,have a range of hyperpa-rameters,the setting of which can affect the retrieval effectiveness.More specifically,all three weighting models have two hyperparameters for each employed documentfield: one related to the term frequency normalisation,and a second one related to the weight of thatfield.As described in Sections3.1and3.2,there are two ways to define the weights offields for the weighting models ML2and M D L2:(i)multiplying the nor-malised frequency of a term in afield;(ii)adjusting the prior probability p i of the i-th field.Thefield weights in the case of PL2F are only defined in terms of multiplying the normalised term frequency by a constant w i,as shown in Equation(6).In this work,we consider only the term frequency normalisation hyperparameters, and we set all the weights offields to1,in order to avoid having one extra parameter in the discussion of the performance of the weighting models.We set the involved hyperparameters c b,c a,and c t,for the body,anchor text,and titlefields,respectively, by directly optimising mean average precision(MAP)on the training tasks from the Web track of TREC2003.We perform a3-dimensional optimisation to set the valuesMultinomial Randomness Models for Retrieval with Document Fields 35of the hyperparameters.The optimisation process is the following.Initially,we apply a simulated annealing algorithm,and then,we use the resulting hyperparameter values as a starting point for a second optimisation algorithm [16],to increase the likelihood of detecting a global maximum.For each of the three training tasks,we apply the above optimisation process three times,and we select the hyperparameter values that result in the highest MAP.We employ the above optimisation process to increase the likelihood that the hyperparameters values result in a global maximum for MAP.Figure 1shows the MAP obtained by ML2on the TREC 2003home page finding topics,for each iteration of the optimisation process.Table 1reports the hyperparameter values that resulted in the highest MAP for each of the training tasks,and that are used for the experiments in this work.0 0.20.40.60.80 40 80 120 160 200M A PiterationML2Fig.1.The MAP obtained by ML2on the TREC 2003home page finding topics,during the optimisation of the term frequency normalisation hyperparametersThe evaluation results from the Web tracks of TREC 2003[3]and 2004[4]have shown that employing evidence from the URLs of Web documents results in important improvements in retrieval effectiveness for the topic distillation and home page find-ing tasks,where relevant documents are home pages of relevant Web sites.In order to provide a more complete evaluation of the proposed models for these two types of Web search tasks,we also employ the length in characters of the URL path,denoted by URLpathlen ,using the following formula to transform it to a relevance score [17]:w d,q :=w d,q +ω·κκ+URLpathlen (12)where w d,q is the relevance score of a document.The parameters ωand κare set by per-forming a 2-dimensional optimisation as described for the case of the hyperparameters c i .The resulting values for ωand κare shown in Table 2.4.2Evaluation ResultsAfter setting the hyperparameter values of the proposed models,we evaluate the models with the search tasks from TREC 2004Web track [4].We report the official TREC evaluation measures for each search task:mean average precision (MAP)for the topic distillation task (td2004),and mean reciprocal rank (MRR)of the first correct answer for both named page finding (np2004)and home page finding (hp2004)tasks.36V.Plachouras and I.OunisTable1.The values of the hyperparameters c b,c a,and c t,for the body,anchor text and titlefields,respectively,which resulted in the highest MAP on the training tasks of TREC2003Web trackML2Task c b c a c ttd20030.0738 4.326810.8220np20030.1802 4.70578.4074hp20030.1926310.3289624.3673M D L2Task c b c a c ttd20030.256210.038324.6762np20031.02169.232121.3330hp20030.4093355.2554966.3637PL2FTask c b c a c ttd20030.1400 5.0527 4.3749np20031.015311.96529.1145hp20030.2785406.1059414.7778Table2.The values of the hyperparameters ωandκ,which resulted in the high-est MAP on the training topic distillation (td2003)and home pagefinding(hp2003) tasks of TREC2003Web trackML2Taskωκtd20038.809514.8852hp200310.66849.8822M D L2Taskωκtd20037.697412.4616hp200327.067867.3153PL2FTaskωκtd20037.36388.2178hp200313.347628.3669Table3presents the evaluation results for the proposed models ML2,M D L2,and the weighting model PL2F,as well as their combination with evidence from the URLs of documents(denoted by appending U to the weighting model’s name).When only the documentfields are employed,the multinomial weighting models have similar perfor-mance compared to the weighting model PL2F.The weighting models PL2F and M D L2 outperform ML2for both topic distillation and home pagefinding tasks.For the named pagefinding task,ML2results in higher MRR than M D L2and PL2F.Using the Wilcoxon signed rank test,we tested the significance of the differences in MAP and MRR between the proposed new multinomial models and PL2F.In the case of the topic distillation task td2004,PL2F and M D L2were found to perform statistically significantly better than ML2,with p<0.001in both cases.There was no statistically significant difference between PL2F and M D L2.Regarding the named pagefinding task np2004,there is no statistically significant difference between any of the three proposed models.For the home pagefinding task hp2004,only the difference between ML2and PL2F was found to be statistically significant(p=0.020).Regarding the combination of the weighting models with the evidence from the URLs of Web documents,Table3shows that PL2FU and M D L2U outperform ML2U for td2004.The differences in performance are statistically significant,with p=0.002 and p=0.012,respectively,but there is no significant difference in the retrieval ef-fectiveness between PL2FU and M D L2U.When considering hp2004,we can see that PL2F outperforms the multinomial weighting models.The only statistically significant difference in MRR was found between PL2FU and M D L2FU(p=0.012).Multinomial Randomness Models for Retrieval with Document Fields37 Table3.Evaluation results for the weighting models ML2,M D L2,and PL2F on the TREC 2004Web track topic distillation(td2004),named pagefinding(np2004),and home pagefinding (hp2004)tasks.ML2U,M D L2U,and PL2FU correspond to the combination of each weighting model with evidence from the URL of documents.The table reports mean average precision (MAP)for the topic distillation task,and mean reciprocal rank(MRR)of thefirst correct answer for the named pagefinding and home pagefinding tasks.ML2U,M D L2U and PL2FU are evalu-ated only for td2004and hp2004,where the relevant documents are home pages(see Section4.1).Task ML2M D L2PL2FMAPtd20040.12410.13910.1390MRRnp20040.69860.68560.6878hp20040.60750.62130.6270Task ML2U M D L2U PL2FUMAPtd20040.19160.20120.2045MRRhp20040.63640.62200.6464A comparison of the evaluation results with the best performing runs submitted to the Web track of TREC2004[4]shows that the combination of the proposed mod-els with the evidence from the URLs performs better than the best performing run of the topic distillation task in TREC2004,which achieved MAP0.179.The performance of the proposed models is comparable to that of the most effective method for the named pagefinding task(MRR0.731).Regarding the home pagefinding task,the dif-ference is greater between the performance of the proposed models with evidence from the URLs,and the best performing methods in the same track(MRR0.749).This can be explained in two ways.First,the over-fitting of the parametersωandκon the training task may result in lower performance for the test task.Second,usingfield weights may be more effective for the home pagefinding task,which is a high precision task,where the correct answers to the queries are documents of a very specific type.From the results in Table3,it can be seen that the model M D L2,which employs the information theoretic approximation to the multinomial distribution,significantly outperforms the model ML2,which employs the multinomial distribution,for the topic distillation task.As discussed in Section3.2,this may suggest that approximating the multinomial distribution is more effective than directly computing it,because of the number of computations involved,and the accumulated small approximation errors from the computation of the factorial.The difference in performance may be greater if more documentfields are considered.Overall,the evaluation results show that the proposed multinomial models ML2and M D L2have a very similar performance to that of PL2F for the tested search tasks. None of the models outperforms the others consistently for all three tested tasks,and the weighting models M D L2and PL2F achieve similar levels of retrieval effectiveness. The next section discusses some points related to the new multinomial models.。

雅思词汇书实用的雅思词汇书对于不少的人来说，选择雅思考试备考书是个大问题，市面上相关的书籍实在太多了，难以选择。

下面，店铺就为大家送上一些值得推荐的雅思词汇书。

1、《雅思词汇词根+联想记忆法》，这本书有大量实用的词根词缀，对于不太擅长记忆长单词的学生来说，这本书能够帮助你理解长单词是如何由词根词缀一步步构成的，同时运用幽默的联想记忆，帮助学生理解如何在句中搭配，使记忆不再枯燥。

听说读写分为四本，使用起来也很有针对性。

2、《剑桥雅思词汇精典—升级版》。

不是按照字母排序，而是按照雅思词汇使用的范围、功能、频率分为基础词汇，核心词汇和认知词汇。

比较符合记忆的顺序，尤其适合基础较弱的考生循序渐进的扩充单词。

同时配有mp3，例句选取的都很实用。

3、《雅思词汇胜经》，此本雅思词汇书是胡敏老师编著的，此书内容是以《剑桥雅思全真试题》3-7的真题为依据，由计算机对5000个左右的核心词汇和短语的出现频率进行了权威、科学的统计，从中选择了高频核心词汇以及中等频率(出现1-5次)的词汇近2500个作为需要识记的词汇进行详细注释。

此外，针对雅思考试中没有出现过，但在将来考试中有可能会出现的一些重点词汇，本书作为预测词汇一并收录。

4、《807雅思词汇》，这本书分听说读写四册。

都是真题高频词和相关扩展词，按场景分类，如听力的租房场景，银行场景词汇，使用起来非常方便，有了场景和话题，单词记忆也更加容易。

我之前曾经介绍过《807听力词汇》，感兴趣的同学可以了解一下。

5、《星火六级单词》，此雅思词汇书当中的很多词汇都是雅思高频出现的.。

附：更多雅思考试备考书籍推荐真题：剑桥4-11、新东方真题精讲4-11点评：因为做题必须要以真题为基准，所以剑桥4-11是必备的。

1-3题太老所以忽略(给刚刚入烤鸭炉的鲜肉烤鸭们解释一下)。

阅读：《雅思阅读真经总纲》——刘洪波、《雅思阅读真经5》——刘洪波、《剑桥雅思阅读考点词真经(剑9版)》——刘洪波点评：刘洪波老师的同义替换理论的确受益匪浅，这三本书连贯性很好，所以希望三本都入。

朗文词典注释所用2000核心词汇集The Longman Defining VocabularyWords used in the definitions in this dictionaryAll the definitions in this dictionary have been written using the words in this list. If a definition includes a word that is not in the list, that word is shown in small capital letters.The Defining Vocabulary has been carefully chosen after a thorough study of all the well-known frequency lists of English words. Furthermore, only the most common and ‘entral’ meanings of the words in the list have actually been used in definitions. We have also used a special computer program that checks every entry to ensure that words from outside the Defining Vocabulary do not appear in definitions.Word class restrictionsFor some words in the list, a word class label such as n or adj is shown. This means that this particular word is used in definitions only in the word class shown. So anger, for example, is used only as a noun and not as a verb. But if no word class is shown for a word, it can be used in any of its usual word classes: answer, for example, is used in definitions both as a noun and as a verb.Compound wordsDefinitions occasionally include compound words formed from words in the Defining Vocabulary, but this is only done if the meaning is completely clear. For example, the word businessman (formed from business and man) is used in some definitions.Prefixes and suffixesThe main list is followed by a list of common prefixes and suffixes. These can be added to words in the main list to form derived words, provided the meaning is completely clear. For example, the word nervousness (formed by adding -ness to nervous) is used in some definitions.Phrasal verbsPhrasal verbs formed by combining words in the Defining Vocabulary (for example, put up with) are not used in definitions in the dictionary, except in a very small number of cases where the phrasal verb is extremely common and there is no common equivalent. So, for example, give up (as in give up smoking) and take off (as in the plane took off) are occasionally used.Proper namesThe Defining Vocabulary does not include the names of actual places, nationalities, religions, and so on, which are occasionally mentioned in definitions.Pearson Education Limited 1999A total of 2043 words,by Qxlee Aa abbreviation abilityableaboutabove adv, prep abroad absence absent adj accept acceptable accident accidental according (to) account n achieve achievement acidacrossactactionactiveactivity actor, actress actual actuallyaddaddition additional address adjective admiration admireadmitadult advanced advantageadventure nadverbadvertiseadvertisementadviceadviseaffairaffectaffordafraidafter adv, conj, prepafternoonafterwardsagainagainstage nagoagreeagreementaheadaimair naircraftairportalcoholaliveall adv, pron,determiner,predeterminerallowalmostalonealongalphabetalreadyalsoalthoughalwaysamongamount namuseamusementamusinganancient adjandanger nangle nangryanimalannounceannoyannoyinganotheransweranxietyanxiousanyanyoneanythinganywhereapartapartmentappearappearanceappleapprovalapproveareaargueargumentarm narmyaroundarrangearrangementarrivalarriveartarticleartificialasas opposed toashamedaskasleepassociationatatomattackattemptattendattentionattitudeattractattractiveauthorityautumnavailableaverage adj, navoidawake adjaway advawkwardbabyback adj, adv, n background backward(s) adv badbag n bake vbalanceball nband nbank nbar nbase n, vbasicbasketbath nbattle nbebeach nbeakbeam nbeanbearbeatbeautifulbeautybecausebecomebedbeerbeforebegin beginning behave behaviour behind adv, prep beliefbelievebellbelongbelowbelt nbendbeneath beside(s)bestbetter adj, adv between beyond adj, adv bicycle n big adjbillbirdbirthbitbitebitter adjblack adj, nbladeblameblind adjblockbloodblowblueboard nboatbodyboilbombbone nbook nboot nborder nboredboringbornborrowbothbottle nbottom nbowl nbox nboybrain nbranch nbrave adjbreadbreak vbreakfast nbreastbreathbreathebreedbrickbridge nbrightbringbroad adjbroadcast vbrotherbrown adj, nbrushbuild vbuildingbulletburnburst vburybusbushbusinessbusybut conjbutter nbutton nbuy vby prepCcake n calculate call v calm adj camera camp n, v cancap capital n carcard carecarefulcarelesscarriagecarrycase ncastlecatcatch vcattlecauseceilingcelebratecellcentralcentre ncenturyceremonycertain adj,determinerchain nchair nchance nchangecharacterchargechase vcheapcheat vcheckcheek ncheerfulcheesechemical chemistry chequechestchickenchiefchildchildrenchin chocolate choice n choose church n cigarette cinemacircle n circular adj citizencityclaim vclass nclayclean adj, v clear adj, v clevercliffclimb vclock nclose adj, adv, v clothclothes clothingcloud nclub n coalcoast ncoat ncoffeecoin ncold adj, ncollar ncollect vcollegecolourcombcombinationcombine vcomecomfortcomfortablecommandcommitteecommon adjcommunicatecommunicationcompanycompare vcomparisoncompetecompetitioncompetitorcomplaincomplaintcompletecompletelycomplicatedcompound ncomputerconcern vconcerningconcertcondition nconfidenceconfidentconfuseconfusingconnectconnectionconsciousconsiderconsistcontaincontainercontinuecontinuouscontract ncontrolconversationcook n, vcopycorncorner ncorrect adj, vcostcottoncoughcouldcouncil ncount vcountry ncountrysidecouragecourse ncourt ncovercow ncrack n, vcrash n, vcrazycream ncreaturecrimecriminalcriticismcriticizecrop ncross n, vcrowdcruelcrush vcrycup ncupboardcurecurlcurrent ncurtain ncurvecustom ncustomercutcycle vDdaily adj, adv damage dance danger dangerous darkdate n daughter daydead adjdeal ndeal withdeathdebtdecaydeceivedecidedecisiondecoratedecorationdecreasedeep adjdefeatdefencedefenddefinitedefinitelydegreedelaydeliberate adjdeliberatelydelicatedeliver demand department depend dependent depth describe description desert n deserve design desirable desiredesk destroy destruction detail n determination determineddevelopdictionarydie vdifferencedifferentdifficultdifficultydig vdinnerdirectdirectiondirtdirty adjdisappointdisappointingdiscoverdiscoverydiscussdiscussiondisease ndish ndismissdistancedistantdividedo vdoctor ndocument ndog ndollardoordouble adj, v,predeterminerdoubtdown adv, prepdraw vdrawerdreamdress n, vdrinkdrive n, vdropdrug ndrum ndrunk pastpart, adjdryduck ndull adjduringdust ndutyEeach eager ear early earn earth n east eastern easy adj eat economic edge n educate educated education effect n effective effort egg eight either elbow elect v election electricelectricityelectronicelseembarrassembarrassingemotionemphasizeemploy vemployeremploymentempty adj, vencloseencourageendenemyenergyengineengineer nenjoyenjoyableenjoymentenoughenterentertainentertainmententrance nenvelopeenvironmentequal adj, nequipmentescapeespeciallyestablisheven adj, adveveningeventevereveryeveryoneeverythingeverywhereevilexact adjexactlyexaminationexamineexampleexcellentexcept conj, prepexchangeexciteexcitingexcuseexerciseexistexistenceexpectexpensiveexperienceexplainexplanationexplodeexplosionexplosiveexpress vexpressionextreme adjextremelyeyeFfacefact factoryfail v failurefair adj fairlyfaith faithful adj fallfalse familiar family famousfarfarm farmer fashion n fashionable fast adj, adv fastenfatfather n fault n favourable favourite adj fear n feather n feature nfeed vfeel vfeeling(s)femalefence nfeverfewfield nfifthfightfigure nfill vfilmfinal adjfinallyfinancialfind vfind outfine adjfinger nfinish vfirefirm adjfirst adj, determinerfishfit adj, vfivefix vflag nflame nflash n, vflat adjfleshflightfloat vfloodfloor nflour nflowflower nfly n, vfoldfollowfondfoodfoot nfootballfor prepforceforeignforeignerforest nforgetforgivefork nformformalformerfortunateforward(s) advfour(th)frame nfreefreedomfreeze vfrequent adjfreshfriendfriendlyfrightenfrighteningfromfront adj, nfruit nfull adjfunfuneralfunnyfur nfurniturefurther adj, advfutureGgain v game n garage n garden gas n gate gather v general adj generally generous gentle gentleman get giftgirlgive vgladglass adj, ngluego vgoatgodgoldgoodgoodbyegoodsgoverngovernmentgracefulgradualgraingramgrammargrand adjgrandfathergrandmothergrandparentgrass ngratefulgrave ngreat adjgreengreetgreetinggrey adj, nground ngroup ngrowgrowthguard vguess vguest nguideguiltygun n Hhabit hairhalfhall hammer n hand n handle hang v happen v happy hard hardly harm harmful hathate v hatred havehe head nhealthhealthyhearheartheatheavenheavy adjheel nheighthellohelphelpfulher(s)hereherselfhide vhigh adj, advhillhimhimselfhishistoricalhistoryhithold vholeholiday nhollow adjholyhome adv, nhonesthonour nhook nhopehopefulhornhorse nhospitalhot adjhotelhourhouse nhow advhumanhumoroushumourhundred(th)hungryhunt vhurryhurt vhusband nIice nideaifignoreill adj illegal illness image imaginary imagination imagine immediately importance important impressive improve improvement in adv, prepincludeincludingincomeincreaseindependentindoor(s)industrialindustryinfectinfectioninfectiousinfluence vinforminformationinjureinjuryink ninnerinsectinsideinsteadinstitutioninstructioninstrumentinsult vinsultinginsuranceinsureintelligenceintelligentintendintentioninterestinterestinginternational adjinterruptintointroduceintroductioninventinvitationinvite vinvolveinwardsiron adj, nislandit pronitsitselfJjaw jewel jewellery job joinjointjokejourney njudgejudgementjuicejumpjust advjusticeKkeen keep v key n kick kill v kilokilogramkilometrekindkingkisskitchenknee nkneelknife nknock vknotknow vknowledgeLlackladylakelamblamplandlanguagelargelast adv, determiner latelatelylaughlaughterlawlawyerlay vlayer nlazylead vleaf nlean v learnleastleatherleave vleftleg nlegallendlength adv, pron,determinerlesslessonlet vlet go ofletterlevel adv, adj, nlibrarylidlielie downlifeliftlightlike prep, vlikelylimitline nlionlipliquidlist nlisten vliteraturelitrelittlelive vloadlocal adjlocklonelylong adj, advlook n, vlook afterlook forlook sth uploose adjlord nloselosslotloudlovelow adjlowerloyalloyaltyluck nluckylungMmachine n machinery magazine magic mail main adj make v make intomake up vmaleman nmanagemanagermannermanymap nmarch vmarkmarket nmarriagemarriedmarrymass nmatchmaterial nmathematicsmattermay vmemealmean v meaning n means measure measurement meat medical adj medicine meet v meeting melt member memory mental mention v messagemetal nmethodmetremiddlemight vmilemiliary adjmilk nmillion(th)mindmine n, pronmineralminister nminute nmirror nmiss vmist nmistake nmix vmixturemodel nmodern adjmomentmoneymonkey nmonthmoon nmoral adjmoremorningmostmother nmotor adj, nmountainmousemouth nmove vmovementmuchmudmultiplymurdermuscle nmusicmusicianmust vmymysteriousmysteryNnailnamenarrow adjnastynationnational adj natural adj naturenavynear adj, adv, prep nearlyneatnecessaryneck needneedle nnegative adjneithernerve nnervousnet nnetwork nnevernewnewsnewspapernext adj, advnicenightnineninthno adv, determinernoise nnone pronnonsenseno onenornormalnorthnorthernnose nnotnote nnothingnoticenoticeablenounnownowherenumber nnursenutOobey object n obtain occasion n oceano&#39;clock ofoff adv, prep offence offendoffensive adjofferofficeofficerofficialoftenoil noldold-fashionedon adv, preponce advoneoniononlyonly justontoopen adj, voperateoperationopinionopponentopportunityopposeas opposed tooppositeoppositionor orange order ordinary organ organize organizationoriginoriginalotheroughtour(s)out adj, advoutdoor(s)outeroutsideover adv, prepoweownowneroxygenPpack v package page npain n painfulpaint paintingpair npale adjpan n paper n parallel adj, n parentpark parliament part n participle particular adj partly partner n party n pass v passage passenger pastpath patient adj pattern n pausepay payment peace peaceful pen n pence pencil n people n pepper nper centperfect adjperformperformanceperhapsperiod npermanentpermissionpersonpersonalpersuadepet npetrolphotographphrase nphysical adjpiano npick vpick uppicture npiece npig npile npilot npinpink adj, npipe npityplaceplain adj, nplanplane nplantplasticplate nplaypleasantpleasepleasedpleasure nplenty pronpluralpocket npoempoetpoetrypointpointedpoisonpoisonouspole npolice npolish vpolitepoliticalpoliticianpoliticspool npoorpopularpopulationport nposition npositivepossesspossessionpossible adjpossiblypossibilitypostpot npotatopound npourpowder npower npowerfulpracticalpracticepractisepraiseprayprayerpreferpreparationpreparepresent adj, npreserve vpresidentpress vpressure npretendpretty adjpreventpreviousprice npriestprinceprincipleprintprisonprisonerprivate adjprize nprobably adjproblemprocess nproduce vproductproductionprofession profit n programme progress n promise pronounce pronunciation proof n properpropertyproposalprotectprotectionprotectiveprotestproudproveprovidepublicpullpumppunishpunishmentpupilpurepurplepurpose npushputQquality quantity quarrel quarter nqueen nquestionquick adjquiet adj, nquiteRrabbit n race radio n railway rainraise v range n rank n rapid adj rarerat nrate n rather raw reach v react reaction read v ready adj real realize really reason reasonable receive recentrecentlyrecognizerecord n, vredreducereductionrefusalrefuse vregard vregular adjrelatedrelativerelationrelationshiprelaxrelaxingreligionreligiousremainremark nrememberremindremove vrentrepairrepeat vreplacereplyreportrepresentrepresentative nrequest nrespectresponsiblerestrestaurantrestrictresultreturn n, vrewardricerichridrideright adj, adv, nring nriseriskriverroadrobrock nroll vromantic adjroof nroom nroot nrope nroserough adjround adj, adv, preprowroyal adjrub vrubberruderuin vrulerulerrunrush vSsad safe adj safetysailsalesalt nsamesand nsatisfaction satisfactory satisfysave vsay vscale n scatter v sceneschool n science scientific scientist scissors screen n screwseasearch season nseatsecond adv, n, determiner secret secretarysee vseed nseemsell vsendsense n sensible sensitive sentence n separate adv, v series serious servantserveservice nset n, vsettle v seven(th) several severesex n sexualshadeshadow nshakeshallshame nshapesharesharp adjshesheepsheetshelfshell nsheltershine vshinyship nshirtshock n, vshockingshoe nshoot vshopshore nshort adjshot nshouldshoulder nshoutshow n, vshutshysick adjside nsidewayssight nsignsignalsilence nsilentsilksillysilversimilarsimplesincesincere nsingsingle adjsingularsink vsistersitsituationsix(th)size nskilfulskillskin nskirt nsky nsleepslide vslight adjslipperyslopeslowsmallsmellsmilesmokesmooth adjsnake nsnowsosoap nsocial adjsocietysock nsoftsoil nsoldier nsolidsolutionsolvesome pron,determinersomehowsomeonesomethingsometimessomewheresonsongsoonsore adjsorrysort nsoulsound n, vsoupsour adjsouthsouthernspace nspeakspecial adjspecificspeechspeed nspell vspendspin vspirit nin spite ofsplit vspoil vspoon nsport nspot nspread vspringsquare adj, nstage nstairstampstand vstandardstar nstartstatestatement station n stay steady adj steal v steam n steel n steep adj stem n stepstick stickystiff adj still adj, adv sting stitch stomach n stone n stop storestorm nstorystraight adj, advstrangestream nstreetstrengthstretch vstrictstrike vstring nstrongstructure nstrugglestudentstudystupidstyle nsubject nsubstancesucceedsuccesssuccessfulsuchsuck vsuddensuffersugar nsuggestsuitsuitablesum nsummersun nsupply n, vsupport n, vsupposesure adjsurface nsurprisesurprisingsurround vswallow vswearsweep vsweetswell vswimswingswordsympatheticsympathysystemTtable ntail ntake vtalktalltastetaxtaxi nteateach team n tear technical telephone television tell temper n temperature temporary tentend tendency tennis tense ntentterribletestthanthankthat conj, pron,determinerthetheatretheir(s)themthen advtherethereforethesetheythick adjthiefthin adjthingthink vthirdthis pron, determinerthoroughthosethoughthoughtthousand(th)thread nthreatthreatenthreateningthreethroatthrough adv, prepthrowthumb nticket ntidy adj, vtietight adjtime ntiredtiringtitletotobaccotodaytoe ntogethertoilettomorrowtonguetonighttootool ntoothtop adj, ntotal adj, ntouchtouristtowardstower ntowntoy ntrack ntrade ntraditionaltraffic n train training translate transparent trap travel treat v treatmenttreetribetrick n, vtrip ntropicaltroubletrouserstrue adjtrusttruthtry vtubetune nturntwicetwisttwotype ntypicaltyreUugly under prep understand underwear undo unexpected uniform n unionunituniteuniverseuniversityunlessuntilunusualup adj, adv, prepupperupright adj, advupset v, adjupside downupstairs adj, advurgentususeusefuluselessusualVvalley valuable adj value n variety various vegetablevehicleverbvery advvictoryview nvillageviolenceviolentvisitvoice nvotevowelWwages waistwait v wake v walkwall nwar n warm adj, v warmth warn warning wash waste watch water waveway weweakwealthweaponwear vweather nweave vweddingweekweekly adj, advweighweight nwelcomewell adj, adv, nwestwestern adjwet adjwhat predeterminer,determiner, pronwhateverwheatwheel nwhen adv, conjwheneverwherewhetherwhichwhile conjwhipwhistlewhitewhowholewhosewhy adv, conjwide adj, advwidthwifewild adj, advwillwillingwin vwindwindowwine nwing nwinterwire nwise adjwishwithwithin without woman wood wooden woolword nworkworldworryworseworstworthwouldwoundwrap vwristwritewrong adj, adv, nYyear yellow yesyetyouyoung adjyour(s)Z zero。

朗文2000解释用词The Longman Defining Vocabulary 2000说明：本词汇表根据《朗文当代英语辞典》（Longman Dictionary of Contemporary English）（外语教学与研究出版社，1997年版）第B16~B22页的词汇表编辑而成。

编辑本表的目的是为了让成年人在最短的时间内掌握能够阅读和使用该词典所必须的有效词汇量。

使用用简明文字进行解释的全英字典对成年人来说最大的好处就是帮助自己实现用英语进行思维，用英语学习英语，在学习中提高，最后掌握地道英语的目标，从而彻底改变“中国式英语”的尴尬局面。

本词汇表的最后还有作者编录的“英语单词常见字母组合表”、“英语常见词缀”、“英语趣味词串”三个附录，目的是为了让学员掌握科学有效的单词记忆法和养成良好的记忆习惯，迅速扩大有用词汇量。

请学员一并学习掌握。

关于其它有效记忆方法，请学员注意在课堂上听讲和介绍。

a 一个，某个（语）abbreviation 缩写ability 能力able 有能力的，能干的about 关于；大约；到处（语）above 在上方（语）above a. 上面的，前述的abroad 在国外absence 缺席accept 接受acceptable 可接受的，尚可的accident 事故accidental 偶然的according to 依照account 考虑；帐户achieve 达成，实现achievement 成就acid 酸across 越过；对面（语）across adv.（语）act 行为，表现；法规act v. 行动action 行动，动作active 活跃的activity 活动actor 演员actress 女演员actual 真实的actually 实际上add 增加；补充说addition 附加，加法additional 附加的address 地址adjective 目标，目的admiration 羡慕，爱慕，崇拜admire 崇拜，爱慕admit 承认adult 成人adult adj. 成人的advanced 先进的，高级的adventure 冒险（事业）；投机adverb 副词advertise （做）广告advertisement 广告advice 忠告advise 劝告affair 事件，桃色新闻affect 影响afford 负担；买得起afraid 害怕after 在…之后（语）afternoon 下午afterwards 然后，以后again 再一次against 对着，反对（语）age 年龄ago 以前agree 同意agreement 协议ahead 向前，前方aim 目标；瞄准air 空气aircraft 飞机airport 飞机场alcohol 酒精alive 活着的all 所有的（语）allow 允许almost 几乎alone 单独（语）along 沿着；和…一起（语）alphabet 字母表，字母顺序already 已经also 也although 虽然always 总是among 在…之间（语）amount 数量amuse 娱乐，使觉得有趣amusement 娱乐，消遣amusing 好笑的，有趣的an 一个，某个（语）ancient 古代的and 和；而；又（语）anger 生气，愤怒angle 角angry 生气的animal 动物announce 宣布annoy 激怒，生气annoying 气人的another 另一个，再一次（语）answer 答案；回答answer v.回答anxiety 焦虑anxious 忧虑的any 任何，一些（语）anyone 任何人anything 任何事anywhere 无论何处apart 分开（语）apartment 公寓appear 出现；显得象appearance 外表，样貌apple 苹果approval 赞成approve 赞同；允许，批准area 区域，地区argue 争论argument 争论，论据arm 手臂army 军队around 环绕，大约（语）arrange 安排，排列arrangement 安排arrival 抵达arrive 到达art 艺术article 文章；冠词artificial 人工的，非真实的as 象；当…时；因为（语）as opposed to 对比，比照ashamed 难堪的；难过的ask 问；请求asleep 睡着的association 协会；联合at 在某点上（语）atom 原子attack 攻击attempt 尝试attend 参加，出席；关怀attention 注意attitude 态度attract 吸引attractive 有吸引力的authority 权威，官方autumn 秋天available 可得的average 平均值；平常的avoid 避免awake 醒着的away 离开（语）awkward 怔住，噎住；难受baby 婴儿，宝贝back后面；回（语）back adj. 后面的adj. background 背景bad 坏的bag 袋子bake 烘，烤balance 平衡；使平衡ball 球band 乐团；宽大的带子bank 银行；河岸bar 棒；酒吧base n. 基地basic 基本的basket 蓝子bath 沭浴battle 战斗be 是；存在（语）beach 海滩beak 鸟嘴，水壶口beam 横梁，光束bean 大豆，豆子bear v. 忍受beat 打，击beautiful 美丽的beauty 美女because 因为become 变成bed 床beer 啤酒before 在…之前（语）begin 开始beginning 起点；初级的behave 举止behavior 行为behind 在…后面（语）belief 信念believe 相信bell 铃belong 属于below 在…下面belt 带子；区带bend 弯曲beneath 在…之下beside 在…旁边besides 除…以外best 最好的better 比较好的between 在两者之间（语）beyond 在……以外；超出bicycle 自行车big 大的；胖的bill 帐单bird 鸟birth 出生bit 一点点bitter 苦的black 黑色blade 刀面，刀身；叶片blame 责骂；过失blind 盲的；使…看不见block 封锁blood 血blow 风吹；打击blue 蓝色board 板；董事会boat 船body 身体boil 烧煮，煮开，沸腾bomb 炸弹bone 骨头book 书boot 长靴border 边界，边缘bored 对…感到厌烦的boring 烦闷的，讨厌的born 出生borrow 借来both 两者（的）bottle 瓶子bottom 底部bowl 碗box 盒子，箱子boy 男孩brain 脑branch 部门brave 勇敢的，大胆的bread 面包break打破breakfast 早餐breast 乳房breath 呼吸breathe 呼吸breed 养育，育种brick 砖块，砖块状物bridge 桥bright 明亮的bring 带来，引起（语）broad 宽广的broadcast 广播brother 兄弟brown 褐色brush 刷子build 建立，建设building 建筑物bullet 子弹，弹子burn 燃烧burst 爆发出，爆破bury 埋葬bus 公共汽车，大巴business 生意，事业busy 忙碌的but 但是buy 买by 在旁边（语）cake 蛋糕calculate 计算；谋划call 呼叫calm 平静的camera 照相机camp 野营；营帐can 能够；可能（语）cap 帽子capital 首都；资本capital adj. 首都的，核心的car 小汽车card 卡片care n. 关心，在意care v. 关心，照看careful 小心的careless 粗心的carriage 马车，车厢carry 搬运，携带case 情形；案子castle 城堡cat 猫cause 原因cause v. 引起，招致ceiling 天花板celebrate 庆祝cell 细胞；电池；监牢center 中心central 中央的century 世纪certain 确定的；某个/些chain 链条chair 椅子chance 机会change 变化character 个性；角色charge 收费；指控；充电chase 追逐，追赶cheap 廉价的（隐含质量差）cheat 欺骗check 检查cheek 颊，脸蛋cheese 乳酪chemical 化工品；化学的chemistry 化学cheque 支票chest 胸chicken 鸡chief adj. 主要的child 孩子children 孩子（复数）chin 下巴chocolate 巧克力choice 选择choose 选择church 教堂cigarette 卷烟circle 圆周；环绕circular 环形的citizen 市民city 城市claim 要求class 班级；阶层clay 粘土，泥巴clean 干净的；清洁clear 清楚的clear澄清clever 聪明的cliff 悬崖climb 攀登clock 时钟close 关闭；接近cloth 布，布料clothes （统称）衣服cloud 云club 俱乐部coal 煤coast 海岸coat 外套；包被coffee 咖啡coin 硬币cold 冷；感冒collect 收集college 学院colo(u)r 颜色comb 梳子，梳头combination 结合combine 结合，叠加come 来（语）comfort 安慰，使感到舒服comfortable 舒服的command 指挥committee 委员会common 通常的communicate 沟通，交往communication 交往；通讯company 公司compare 比较comparison 比较compete 竞争competition 竞争competitor 竞争者，对手complain 抱怨complaint 诉苦，怨言complete adj. 完全的complete v. 完成completely 完全地complicated 复杂的compound 化合物computer 计算机concern v. 关注concerning关于concert 音乐会condition 情况，条件confidence 信心confident 有信心的confuse 使迷惑，混淆confusing 令人迷惑不解的connect 连接connection 连接conscious 有意识的consider 考虑consist of 由……组成container 容器continue 继续continuous 连续的contract n. 合同，合约control 控制，控制装置control v. 控制v. conversation 交谈，对话cook 烹调；厨子copy 副本；复制corn 谷物，玉米corner 角落correct 正确的cost 费用cost v. 花费cough 咳嗽could 可能；能够（语）council 委员会，顾问组count 计数；看成是country 国家countryside 乡下地方courage 勇气course 课程court 法院cover n. 覆盖物，盖子cover v. 包括v.cow 母牛，奶牛crack 开裂，裂口crash 坠毁，砸向crazy 发狂的cream 膏creature 活物；动物；生物crime 罪行criminal 罪犯criticism 批评criticize 批评crop 农作物cross 十字架；交叉；越过crowd 群众cruel 残忍的crush 压碎，碾碎cry 哭，大呼大叫cup 杯子cupboard 厨柜，碗柜cure 治疗curl 卷曲current水流；流行的curtain 帘子(=drape, 美式)curve 曲线custom 习惯customer 客户cut 切，砍cycle 周期，循环daily n. 日报damage 损害dance 跳舞danger 危险dangerous 危险的dark 黑暗date 日期；定日子daughter 女儿day 一天时间；日子dead 死的deal 交易deal with 处理，对付，解决death 死亡debt 债务decay 腐败，腐烂，变质deceive 欺骗，蒙蔽decide 决定decision 决定decorate 装饰decoration 装潢，装饰品deep 深的defeat 败北；打败，击垮defend 防御，抵御defense, -ce 防卫，抵御definite 明确的，确定的definitely 明白无误地，明确地degree 程度delay 延迟deliberate 深思熟虑的，故意的deliberately 故意地delicate 精细的，娇弱的deliver 递送demand 要求department 部门depend 依靠；取决于dependent 不能独立的；赡养人depth 深度describe 描述description 描述desert 沙漠deserve 应得design 设计desirable 人人都渴望的desire 渴望desk 书桌destroy 破坏，摧毁destruction 破坏detail 细节determination 决心determined 有决心的，坚决的develop 发展，开发，形成dictionary 字典，词典die 死difference 不同，差别，差额different 不同的difficult 困难的difficulty 困难dig 挖，掘dinner 晚餐direct 直接的direction 方向dirt 泥土，尘土，灰尘dirty 肮脏的disappoint 使失望disappointing 令人失望的discover 发现discovery 发现discuss 讨论discussion 讨论disease 疾病dish 碟子dismiss 开除；遣散distance 距离distant 远的divide 分开；除do 做（语）doctor 医生document 文件dog 狗dollar （货币单位）元door 门double 加倍doubt 怀疑down 向下（语）draw 牵拉，拖拉；提款drawer 抽屉dream 梦；做梦dress 服装，衣着dress v. 穿着；打扮drink n. 饮料drink v. 喝drive 开车；驱赶drive 驾驶，驱动drop 扔；急落/降drug 药；毒品drum 鼓drunk 喝醉了的dry干的；弄干duck 鸭子dull 忧郁的，阴暗的during （语）dust 灰尘duty 责任each 各个，逐个（语）eager 急切的ear 耳朵early adj. 早的early adv. 以前earn 赚得earth 地球east 东方eastern 东方人easy 容易的eat 吃economic 经济的edge 边缘educate 教育，培养educated 受过教育的education 教育effect 效果，作用，影响effective 有效的effort 努力egg 蛋eight 八either 两者之一（语）elbow 肘elect 当选人；选举election 选举electric 电的electricity 电electronic 电子的else 所有其它的（语）embarrass 使丢脸，使局促embarrassing 令人难堪的emotion 情绪，情感emphasize 强调employ 雇用employer 雇主employment 雇用，就业empty 空的enclose 围住，封闭encourage 鼓励end 结束；终了enemy 敌人energy 能量，能源engine 引擎engineer 工程师enjoy 享受enjoyable 令人开心的，可享受的enough足够的；足以enter 进入entertain 娱乐，给人以娱乐entertainment 娱乐entrance 入口envelop 信封environment 环境equal adj. 相等的equipment 设备；元件escape 逃亡，逃逸especially 尤其establish 建立even 甚至；均匀的（语）evening 晚上event 事件ever 曾经every 每一个（语）everyone 每个人everything 任何东西，一切（语）everywhere 各处evil 邪恶exact 确切的，正确的exactly 确切；完全examination 考试examine 调查example 例子excellent 极好的except 除……外exchange n. 交换；外汇excite 使激动，使兴奋exciting 刺激excuse 原谅，饶恕exercise 练习exist 存在existence 存在expect 预期，指望，盼望expensive 昂贵的experience 经验；经历explain 解释explanation 解释explode 爆炸explosion 爆炸explosive 炸药；有爆炸性的express 表达；高速公路expression 表达；表情extreme 极端extremely 极端地eye 眼睛face 脸；面对fact 事实factory 工厂fail 不及格；失败failure 失败；断水，停电fair adj.公平的fairly 公平地faith 信念faithful 忠实的fall 落下；跌倒；秋天false 错误的，假的familiar 熟悉的family 家庭，家族famous 有名的far 远（语）farm 农场farmer 农民fashion 流行fashionable 流行的，时髦的fast 快fast adv. 快；紧紧地fasten 系紧，加固fat 脂肪；肥的father 父亲fault 过失，毛病favo(u)rite 最喜爱的（事物）fear 恐惧feather 羽毛feature 特征feed 喂养，饲养；进料feel 感觉；抚摸feeling 感觉，感情female 女性fence 栅栏，篱笆fever 发烧；兴奋few 几个（语）field 领域，田地fifth 第五fight n. 战斗，打架figure 人物；外形fill 填充film 电影final 最后的finally 最后financial 财政的find 找；发现find out 找到，找出来fine 很好的finger 手指finish 完成fire 火；开火firm adj. 坚定的first 第一；最初fish 鱼fit adj. 适宜的fit v. 修理five 五fix 固定，维修flag 旗子，旗帜flame 火焰flash 闪烁；闪光flat adj. 扁平的flesh 肉，肉体flight 飞行float 漂浮flood 洪水；淹没；泛滥floor 地板flow n. 水/电/气流flower 花；开花fly 飞行；飞虫fold 折叠；皱褶follow 跟随fond 喜欢的food 食物foot 脚football 足球for prep. 为，给，向（语）force n. 力force v. 迫使foreign 外国的，陌生的foreigner 外国人forest 森林forget 忘记forgive 饶恕fork 叉子form n. 形式；表form v. 形成formal 正式的former 先前的；前者fortunate 幸运的forward adv. 向前four 四fourth 第四free 自由的freedom 自由freeze 微风，吹微风frequent 时常发生的fresh 新鲜的friend 朋友friendly 友好的frighten 威吓frightening 吓人的from 来自…（语）front adj. 前面的front n. 前面，面前fruit 水果full 满；全部的fun好玩，有趣funeral 葬礼funny 好笑的，好玩的fur 动物的皮毛，毛皮furniture 家具further 较远的；进一步future未来gain 获取game 游戏；比赛；运动会garage 车库garden 花园gas 气体；汽油gate 大门gather 聚集general 一般的，总的generally 总的来说generous 宽宏的，大度的gentle 温和的，温柔的gentleman 绅士，男士get 得到，变得（语）gift 礼物girl 女孩give 给（语）glad 高兴的glass 玻璃；玻璃杯glue 胶水go 走（语）goat 山羊god 上帝gold 黄金good 好的（语）goodbye 再见goods 商品，货物govern 统治government 政府graceful 优雅的gradual 逐渐的grain 粮食，谷物gram （重量单位）克grammar 语法grand 壮观的，宏大的grandfather 祖父grandmather 祖母grandparent 祖父母grass 草grateful 感谢的，感激的great 伟大的，很棒的green adj. 绿色的greet 打招呼，问候greeting 问候语，问候grey, gray 灰色ground 地面group 团体grow 生长growth 生长guard守卫guess 猜测guest 客人guide n. 指导，向导guilty 有罪的gun 枪habit 习惯hair 头发，毛half一半（的）hall 大厅hammer 锤子；用锤子敲hand 手；递；交handle 把柄，把手；处理hang 悬挂happen 发生happy 快乐的hard 困难的；硬的；努力hardly 刚刚，几乎不harm 伤害harmful 有害的hat （有边的）帽子hate 憎恨hatred 仇恨，厌恶have 有（语）he 他head 头health 健康healthy 健康的hear 听到heart 心heat 热；加热heaven 苍天；上天heavy 重的heel 脚后跟，踵height 高度hello 喂；你好help 帮忙helpful 有帮助的her 她here 这里herself 她自己hide 躲藏，藏匿high 高的hill 小山him 他himself 他自己his 他的historical 历史的hit 碰撞，击打hold 握；容纳；举行hole 洞holiday 假日hollow 中空的holy 神圣的home 家home adv. 在家honest 诚实的hono(u)r 荣誉，光荣hook 钩子hope 希望hopeful 有希望的，满怀希望horn （汽车）喇叭horse 马hospital 医院hot 热的hotel 旅馆，宾馆hour 小时house房子how 如何（语）human人类（的）humour 幽默hundred 一百hundredth 第一百hungry 饥饿的hunt 狩猎，打猎hurry 匆忙，急忙；赶快hurt 伤害husband 丈夫ice 冰idea 主意，想法if 假如；是否（语）ignore 不理，忽视ill 有病的，病态的；坏illegal 非法的illness 疾病image 图像imaginary 虚构的，想象出来的imagination 想象力imagine 想象immediately 立刻importance 重要性important 重要的impressive 给人深刻印象的improve 改善，提高improvement 进步，改善in 在…里面（语）in spite of 不管怎样，尽管include 包括including 包括income 收入increase 增加independent 独立的indoors 室内的；在室内industrial 工业的，行业的industry 工业，行业，实业infect 传染，感染infection 传染，感染infectious 传染性的influence影响inform 告知information 数据，信息injure 受伤，负伤；伤害injury 受伤ink 墨水inner 内部的insect 昆虫inside （语）instead 而不是，改为institution 机构instruction 指令instrument 仪器insult 侮辱，对人无礼insulting 无礼的，出口伤人的insurance 保险insure 为…投保/作保intelligence 智力intelligent 智力的，有智慧的intend 打算；目的是intention 动机，目的，意图interest 兴趣interesting 有趣的international 国际的interrupt 干预，妨碍，插嘴into 进入（语）introduce 介绍，引进introduction 介绍invent 发明，创造，创作invitation 邀请，请柬；引诱invite 邀请involve 包括inwards 向内（的），内心（的）iron 铁；烫斗island 岛it 它（语）its 它的jaw 颚，颌jewel 珠子jewellery 珠宝，珠宝业job 工作join 加入；合伙joint 关节joke 笑话journey 旅途judge 法官；裁决，裁判judgment 裁判,判断juice 果汁，果浆jump 跳跃just 刚刚，仅仅（语）justice 正义，公平；司法keen (eager) 急切的keep 保持，维持key n. 钥匙；问题的关键kick 踢kill 杀死kilo 千，千单位kilograme 千克kilometer, -re 千米kind 类型；好的king 国王kiss 吻，亲kitchen 厨房knee 膝；跪kneel 跪下，下跪knife 刀子knock 敲，击knot 结，突起；打结know 知道knowledge 知识lack 缺少，缺乏lady 女士，太太lake 湖lamb 羊羔，羊肉lamp 有灯座的灯；照亮land n. 陆地，土地language 语言large 大的last最后的；上一个（语）late adj. 迟的，晚的（语）lately 最近，近来laugh 笑laughter 笑声law 法律lawyer 律师lay 铺设；产卵layer 层lazy 懒惰的lead v. 带领，引导，导致leaf 叶lean 斜靠learn 学习；了解least 最少leather 皮革leave 离开，把…留下left adj. 左边的leg 腿legal 法律的；合法的lend 借给length 长度less adv. 更少，很少（语）lesson 课let 让，允许（语）let go of 松开，放开letter 信；字母level adj. 平的（不倾斜），同高的level n. 水平library 图书馆lid 盖子lie谎言; 平躺; 摆放;撒谎lie down 躺下life 生活；生命lift v. 举起，提起light v. 点着（灯、火）like prep. 象…一样（语）likely 有可能的limit n. 限度line 线，队列lip 唇liquid 液体，液list n. 清单，名单listen 听literature 文学litre, -er （体积）升little小的；少的；少少（语）live /liv/ v. 过（生活）load 负荷local 当地的lock v. 上锁lonely 孤单的，寂寞的long adj. 长的look看；看起来（语）look after 照看，照料look for 寻找look somethingup查阅，查字典loose adj. 疏松的，松散的lord n. 主人；上帝lose 失去loss 损失lot 大量loud 大声的love v. 爱，喜欢low 低lower 降低，放低loyal 忠诚的，忠实的loyalty 忠贞，忠诚，忠实luck 运气lucky 幸运的lung 肺，肺部machine 机器machinery 机械类，车床magazine 杂志magic 魔术mail n. 邮件main 主要的make 做，制，弄（语）make into 转变，做成make up 合成；决定male 男性，雄性male n. 男性man 男人，人类（语）manage 设法做成/完成/实现manager 经理manner 样子，方式many 很多map 地图march 行进，行军，前进mark n. 标记market 市场marriage 婚姻married 已婚的mass n. 大众；（物理）质量match n. 火柴；淘汰赛；般配match v. 相配v.material 材料mathematics 数学matter n. 问题，事情；物质may 可以；可能me 我meal 饭菜mean v. 意思是meaning 意义means 方法，手段measure 尺寸；测量尺寸measurement 测量，测量值meat 肉medical 医学的medicine 医药；临床医学meet 遇到，见到meeting 会议melt 熔化member 成员memory 记忆mental 心智的mention v. 提到，述及message 信息metal 金属meter, -re （长度单位）米method 方法middle adj. 中间的might 可能；可以（语）mile 英里military 军队；军事的milk 奶million 百万mind n. 头脑mind v. 介意mine矿井；采矿，开采mine 我的东西mineral 矿物质，矿物的ministry （政府的）部minute 分钟mirror 镜子misician 音乐家miss v. 想念mist 雾mistake 错误；搞错mix 混合，使混合mixture 混合物model 模型modern 现代的moment 片刻money 钱monkey 猴子month 月moon 月亮moral 道德（的）more 更多的（语）morning 早上most 最多的，大多数（语）mother 母亲motor 马达，发动机mountain 山mouse 老鼠mouth 嘴move 移动；使感动movement 运动much 很多mud 泥巴multiply （算法）乘murder 谋杀muscle 肌肉music 音乐must 必须my 我的mysterious 神秘的mystery 奥秘，神秘nail 钉子name名字narrow 狭窄的nasty 肮脏的，下流的nation 国家，民族national 国家的，民族的natural 自然的，天然的nature 自然navy 海军near 接近nearly 几乎neat 整洁的necessary 必要的neck 脖子need 需要needle 针，细而尖的东西negative 否定的neither 也不（语）nerve 神经nervous 神经紧张的net网；用网捕鱼network 网络never 从不new 新的news 新闻，消息newspaper 报纸next下一个；在…之后next adv. 紧挨着nice 美好的night 夜晚ninth 第九no 不；无no one 没有一个，一个也不noise 噪音none 无，没有一个nor 也不normal 正常的north 北方northern 北方的nose 鼻子not 不note 注意；字条nothing 无notice n. 注意notice v. 注意noticeable 显著的，值得注意的noun 名词now 现在nowhere 哪里都不/没有number 数字nurse 护士；护理nut 坚果，硬果核o’clock 点钟obey 服从，遵从object 物体；宾语obtain 获得occasion 场合ocean 海洋，大洋of …的（语）off 关闭，离开（语）offence, -se 犯法，冒犯；过错offend 冒犯，违背，挑衅offensive 令人不快的，挑衅的offer n. 供应；机会office 办公室officer 官员official 官方的，正式的often 时常oil 油old 老；旧old-fashioned 陈旧的，老式的on 在…面上（语）once 一旦；曾经；一次one 一；某人onion 洋葱，大葱only 唯一的；仅仅（语）only just 仅有那么一点；刚刚onto 到……上面open adj. 公开的；开放的open v. 打开operate 操作；运行operation 操作；手术opinion 意见，观点opponent 对手，敌手；对立的opportunity 机会oppose 反对，反抗opposite 对面；相反的opposition 反对or 或orange 橙，桔子order n. 顺序；订单；指令ordinary 平常的organ 器官，机构organization 组织，机构organize, -se 组织origin 起源，根本，源头original 本来的，原创的other 另一些，其他的ought 应该our(s) 我们的（东西）out在…外面，向外（语）outdoor(s) 户外的，在户外outer 外部的，外层的outside在…外部over在…上方（语）owe 亏欠；归功于own 自己的；拥有owner 所有者，业主oxygen 氧气pack v. 打包装package 包裹page 页pain 疼痛painful 疼痛的，痛苦的paint 油漆；油画painting 油画pair 双，对pale 苍白的pan 炒菜锅paper 纸parallel 平行的；平行于parent 父母park 公园parliament 国会part 部分participle 分词particular 特别的partly 部分地partner 合伙人party 聚会；党派；各方pass v. 经过；递passage 通道；篇章passenger 乘客past adj. 过去的path 路径patient adj. 有耐心的pattern 式样pause 中止，暂停pay 支付；给予…pay付款（语）payment 付款，款额；报应peace 和平peaceful 和平的pen 笔，钢笔pence （货币）便士pencil 铅笔people 人们；民族per cent 百分之perfect 完美的perform 表演，操作performance 表现perhaps 或许period 时期permission 许可，同意person 人personal 个人的persuade 劝pet 宠物petrol 汽油photograph 相片phrase 词组physical 身体的; 物理的;天体的piano 钢琴pick 挑选；拾；捡；接人pick up 拾起，挑选，收获picture 照片，图片piece 块，片，件pig 猪pile 堆；堆积pilot 飞行员pin 大头针，别针pink 粉红色pipe 管子；用管道输送pity 遗憾，同情，怜悯place n. 地方plain adj. 明明白白的；简朴的plan计划plane 飞机plant 植物；工厂plastic 塑料plate 盘子play戏，剧play v. 玩；打球；演奏pleasant 令人愉快的please 请；使高兴pleased 高兴的pleasure 高兴，高兴的事plenty 充分plural 复数pocket 口袋poem 诗poet 诗人poetry 诗歌，韵文point n. 点pointed 尖的，锐利的poison 毒，毒物poisonous 有毒的pole 竖立的杆/柱子police 警察polish 擦亮polite 礼貌的，有礼貌的political 政治上的politician 政客politics 政治（学）pool 池poor 贫穷的；差的popular 流行的population 人口port 港口position 位置possess 拥有possession 财产，占有possibilty 可能性possible 可能的possibly 可能地post 邮政；邮件；寄送pot 锅，罐，壶potato 土豆，马铃薯pound 磅pour 倾倒，大量注入powder 粉末power 力量，功率powerful 强有力的practice n. 练习，实践practice, -se v. 练习，实践praise 赞扬，表扬，称赞pray 祈祷，做祷告prayer 祈祷，祷告prefer 宁要，更喜欢preparation 准备，预备prepare 为…作准备；制备present adj. 现在的present n. 现在；赠礼. preserve 保存，保护；保护区president 总裁；总统press v. 压，印，按pressure 压力pretend 假装pretty adj. 漂亮的prevent 阻止；防治previous 早先的price 价格priest 牧师prince 王子，亲王principle 原理print n. 印刷品prison 监狱prisoner 囚犯private 私人的，秘密的prize 奖赏，奖金probably 或许problem 问题process 程序produce 生产product 产品production 生产过程profession 技术型职业profit 利益program(me) 程序；节目progress 进步promise n. 承诺，诺言promise v. 许诺pronounce 发音pronunciation 所发的音，发音法proof 证据proper 适当的property 财产proposal 提议protect 保护protection 保护protective 保护性的，有保护作用的protest 抗议proud 骄傲的prove 证明provide 提供public 公开的；公众public n. 公众pull拉pump 泵punish 处罚，责罚punishment 处罚pupil 小学生；瞳孔pure 纯粹的，地道的purple 紫色purpose 目的push 推put 摆，放，置，搞（语）quality 质量quantity 数量quarrel 争吵，吵架quarter 四分之一queen 皇后question问题quick快的quiet 安静quite 相当，十分rabbit 兔子race n. 竞走radio 无线电railway 铁路rain雨；下雨raise 使升高，提升range n. 范围；谱rank n. 等级，地位rapid 迅速的rare 稀罕的rat 老鼠rate n. 比率rather 宁可raw 生的，未加工的reach 到达；够得着react 反应reaction 反应read 阅读；原文是…ready 准备好的，备用的real 真正的realize 认识到really 真正reason 理由，原因reasonable 合理的receive 接受recent 最近的recently 最近recognize 承认，公认，辨认record n. 记录record v. 记录red 红色reduce 减少reduction 减少refusal 拒绝refuse 拒绝regard v. 把…视为regular 有规律的related 相关的relation 关系relationship 关系relax 放松relaxing 让人放松的religion 宗教religious 宗教的remain 余下，遗留remark n. 评论；言论remember 记得remind 提醒remove 除去，撤去rent 租用；租金repair n. 修理repeat 重复replace 替换reply 答复report n. 报告n.report v. 汇报v. represent 表现；描绘representative 代表request 请求respect 尊敬responsible 有责任的rest n. 休息restaurant 餐厅restrict 限制result n. 结果return 回，返reward 奖赏；奖品rice 大米，米饭，水稻rich 富有的rid 摆脱，免除，舍弃ride v. 骑right adj. 正的；正确的；右边的right adv. 正确；完全right n. 权利ring n. 环；轮；戒指rise v. 上升risk 危险；冒险river 河road 道路rob 抢劫rock 岩石roll 卷romantic 浪漫的，爱情的roof 屋顶room 房间；空间，地方root 根rope 粗绳rose n. 玫瑰；玫瑰色rough 粗糙，汹涌；粗鲁round adj. 圆的round adv. 围绕round prep. 围绕row n. 行，排royal 王室；皇家的rub 擦，摩擦rubber 橡皮擦，橡胶rude 粗鲁的ruin 毁灭，堕落rule n. 规则ruler 尺子run v. 跑；运转；经营rush 冲刺，急奔；匆忙的sad 伤心的，悲哀的safe 安全的；保险柜safety 安全sail 帆；航行sale 售卖salt 盐same相同的（语）sand 沙子satisfaction 满足satisfactory 满意的，满足的satisfy 使满足/满意save 节俭；饶恕；救援say 说scale 刻度；规模scatter 飞溅，散射，散开scene 现场，场景school 学校science 科学scientific 科学的scientist 科学家scissors 剪刀screen 荧屏；屏蔽screw 螺丝，螺钉sea 大海search搜寻season 季节seat 座位second第二；秒secret 秘密secretary 秘书，书记see 看到seed 种子seem 像是，似乎sell 卖send 发送，派出sense 感觉，感知sensible 明智的；意识到的sensitive 敏感的sentence 句子separate adj. 分别的，各自separate v. （使）分开series 系列serious 严重的，严肃的servant 仆人serve 侍奉；上菜；作…用service 服务set n. 一套东西set v. 设定settle 解决；定居seven, -th 七，第七several 一些severe 严酷的sex 性别sexual 性（别）的shade 荫蔽处，暗处shadow 阴影shake 摇动shall 将shame 羞愧，惭愧shape 形状；形成share v. 分享sharp 尖锐的she 她sheep 绵羊sheet 纸张，羊皮shelf 架子shell 贝壳shelter 遮蔽，遮蔽处shine 照射shiny 阳光照耀的ship 船shirt 衬衫shock 震惊，冲击shocking 令人震惊的shoe 鞋shoot 射击shop 商店，车间shore 海滩，河滩short 短shot 射击，一击should 应该shoulder 肩shout 呼喊show 展现；给看show n. 表演shut 关上shy 害羞的，扭捏的sick 有病的，感觉难受的side 边，侧面sight 视力sign n. 迹象，症状signal 信号silence 沉默，安静silent 安静的，沈默的silk 丝绸，蚕丝similar 相似的，类似的simple 简单的since自从；既然sing 唱single 单一的，单身的singular 单数sink 下沉；洗涤槽sister 姐妹sit 坐situation 情形，场合six, sixth 六，第六size 大小，规格，尺寸skilful 有技巧的，高水平的skill 技巧，本领skin 皮肤skirt 裙子sky 天空sleep n. 睡觉sleep v. 睡觉slide 滑动，滑行slight 轻微的，些许的slippery 滑的slope 倾斜slow adj. 慢的small 小的smell 气味smell v. 闻…的气味；闻起来smile n. 微笑smile v. 微笑smoke n. 烟雾smoke v. 吸烟smooth 平滑的，光滑的snake 蛇snow 雪，下雪so 如此；因此soap 肥皂social 社会的society 社会sock 袜子soft 柔软soil 土壤soldier 士兵solid 固体；坚实的solution 解决办法solve 解决，溶解some 一些；某个somehow 不知何故someone 某人something 某物sometimes 有时somewhere 某处son 儿子song 歌soon 很快，不久sore 悲伤的，痛苦的sorry 难过的；对不起sort种类soul 灵魂sound 声音；听起来soup 汤sour 酸的，酸痛的south 南方southern 南方的space 空间，太空speak 说special 特别的specific 特定的speech 演讲speed n. 速度spell 拼读，拼写spend 花（钱、时间）spin 旋转spirit 精神split 分裂，裂解spoil （使）变质；损坏sport 运动spot n. 斑点spread 散布spring 春天；泉水square adj. 正直的，正方形的stage 阶段；舞台stair 梯级stamp邮票；盖章；践踏stand 站立；忍受standard 标准standard adj. 标准的star 星，恒星start n. 出发点，开端start v. 开始state n. 国家；状态state v. 陈述，声明，指出statement 陈述；断言station 车站stay 停留，逗留steady 稳固的，稳定steal 偷steam 蒸气steel 钢steep 陡峭的step n. 步伐；梯级step v. 行走，踏足于stick n. 枝条，棍子，手杖sticky 有粘性的stiff 坚硬的，呆板的still 仍然；静止的sting 叮，咬，蜇stitch 织，缝；织针，针步stomach 胃，肚子stone 石头；单粒的沙子stop n. 停止；停车站点stop v. 停止；阻止；防止store小商店，士多store v. 储存，贮藏storm 暴风雨，风暴story 故事straight adj. 笔直的，直率的straight adv. 笔直strange 陌生的，奇怪的stream 流，小河，溪流street 街道strength 力量strict 严格的，严厉的strike v. 棒揍，敲打；袭击string 线，铉strong 强壮的structure 结构struggle努力，奋斗，挣扎student 学生study n. 学习study v. 学习，研究stupid 愚蠢的style 风格，款式subject 科目，课题，受试者substance 物质；实质，本质succeed 成功success 成功successful 成功的such 如此的suck 吸，吮吸sudden 突然的suffer 遭受…痛苦，承受打击sugar 糖suggest 提议，建议；让人想到suit n. 套，组；套装，西装suit v. 适合于suitable 适当的sum 总数；和数summer 夏天sun 太阳supply n. 供应，供给supply v. 供应，提供，满足support n. 支持support v. 支持suppose 假如，假定sure 确信surface 表面surprise n. 吃惊，惊喜surprising 令人惊讶的surround v. 包围swallow 燕子；吞咽swear 发誓sweep 扫除；打扫sweet adj. 甜蜜的，漂亮的swell 肿胀，膨大swing 摇摆，挥动，挥舞sword 剑sympathetic 同情的，有同感的sympathy 同情，同感system 系统；制度；体制table 桌子tail 尾部；尾随，盯梢take 拿talk 谈话tall 高的taste 滋味；品尝味道tax 税taxi 出租车，计程车tea 茶teach 教team 队，小组tear n. 眼泪tear v. 撕烂technical 技术上的telephone 电话television 电视tell 告诉；辨别temper 脾性，脾气temperature 温度temporary 暂时的，一时的ten 十tend 倾向于；有…趋势tendency 趋向，天份，潮流tennis 网球; (table ~乒乓球)tense 时态tent 帐篷terrible 可怕的，严重的test n. 测试test v. 检测，测试than比较thank谢谢that 那个（语）the 这个，那个（语）theater, -re 戏院their 他/她/它们的them 他/她/它们then 然后；那时there 在那里（语）therefore 因此these 这些they 他/她/它们thick 厚的thief 小偷，贼thin 瘦的thing 事情，东西think 想，认为third 第三this 这（语）thorough 彻底的，完全的those 那些though 然而，可是thought 思想，想法thousand, -th 一千，第一千thread 线，索threat 威胁threaten 对…构成威胁threatening 有威胁的three 三throat 咽喉through 穿过throw 抛，扔，投，丢thumb 大拇指ticket 票tidy 整洁的，漂漂亮亮的tie n. 领带，绳索，带子tie v. 系紧，捆绑tight 紧的，不松动的time 时间，时代；定时tired 疲劳，累tiring 累人的title 名称，衔头to 去到，对着（语）tobacco 烟草，烟叶，香烟today 今天toe 脚趾头together 一起toilet 洗手间，盥洗室tomorrow 明天tongue 舌；（俗）语言tonight 今晚too 也；过于tool 工具tooth 牙齿top顶部top adj. 最高的total adj. 总的total n. 总额touch n. 接触touch v. 触摸tourist 旅游者，观光者towards 向着，对着（语）tower 塔，塔楼，高楼town 城镇toy 玩具track 轨道trade n. 贸易traditional 传统的traffic 交通train n. 火车train v. 训练training 训练translate 翻译transparent 透明的trap 陷阱；陷入困境travel n. 出行，旅游travel v. 旅行，出门去treat 处理，对付treatment 治疗tree 树tribe 部落，部族trick 技巧，骗术，花样trip 旅行过程tropical 热带的trouble 麻烦trousers 裤子true 真实的trust n. 信任trust v. 信任，相信truth 事实try 尝试tube 管子，筒tune 曲子，旋律；校对turn n. 轮换；当值turn v. 转动twice 两次type 类型；打字typical 典型的tyre, tire 轮胎ugly 丑陋的，难看的under 在…下方（语）understand 懂得，明白，了解underwear 内衣undo 解开，取消，破坏unexpected 意想不到的uniform 制服；统一的union 联盟unit 单位，单元unite 统一，使统一起来universe 宇宙university 大学unless 除非，如果不until 直到unusual 不寻常的，奇特的up 向上（语）upper 上面的upright 正直的，垂直的upset 烦乱的；烦躁的upside 上侧，上边，上端upstairs 搂上的，在楼上urgent 紧急的，紧迫的us 我们use使用use n. 用途，用法useful 有用的useless 没用的usual 平常的valley 山谷；流域valuable 有价值的，宝贵的value 价值variety 多样性various 各种的vegetable 蔬菜vehicle 交通工具verb 动词，谓语very 很，非常victory 胜利view n. 观点village 村庄violence 暴力violent 猛烈的；暴力的visit拜访，访问voice 声音vote n. 选票，表决vote v. 投票，选举vowel 元音wage 薪水，报酬waist 腰部wait 等候，等待wake 醒来，叫醒walk n. 散步walk v. 走路，散步wall 墙壁want 需要，要war 战争warm 温暖的warmth 温暖warn 警告warning 警告wash 洗，冲刷，淋洗waste adj. 无用的，废弃的waste n. 垃圾，废物waste v. 浪费watch n. 手表watch v. 观看，注意water n. 水wave n. 波动，浪way 道路；方法，手段we 我们weak 弱的wealth (指实业、房产等的)财富weapon 武器wear 穿；戴weather 天气weave 编织wedding 婚礼week 星期weekly 每周的；周报/刊weigh 重达，称重weight 重量welcome adj. 受欢迎的welcome v. 欢迎某人well adv. 好；非常well n. 井west 西方western 西方人wet 潮湿的；湿气what 什么whatever 无论什么wheat 小麦wheel 轮；方向盘；舵轮when 当…时候whenever 只要到了…时候，每当where 哪里whether 是否。