Near-Optimal conversion of Hardness into Pseudo-Randomness
Russell Impagliazzo Computer Science and Engineering
UC,San Diego
9500Gilman Drive
La Jolla,CA92093-0114
russell@https://www.doczj.com/doc/359850529.html,
Ronen Shaltiel Department of Computer Science Hebrew University
Jerusalem,Israel ronens@cs.huji.ac.il
Avi Wigderson
Department of Computer Science
Hebrew University
Jerusalem,Israel
avi@cs.huji.ac.il
Abstract
Various efforts([3,5,6,9])have been made in recent
years to derandomize probabilistic algorithms using the
complexity theoretic assumption that there exists a prob-
lem in,that requires circuits of size
,(for some function).These results are based on
the NW-generator[7].For the strong lower bound
,[6],and later[9]get the optimal derandomization,
.However,for weaker lower bound func-
tions,these constructions fall far short of the nat-
ural conjecture for optimal derandomization,namely that
.The gap in these con-
structions is due to an inherent limitation on ef?ciency in
NW-style pseudo-random generators.
In this paper we are able to get derandomization in al-
most optimal time using any lower bound.We do this
by using the NW-generator in a new,more sophisticated
way.We view any failure of the generator as a reduction
from the given“hard”function to its restrictions on smaller
input sizes.Thus,either the original construction works
(almost)optimally,or one of the restricted functions is(al-
most)as hard as the original.Any such restriction can then
be plugged into the NW-generator recursively.This process
generates many“candidate”generators,and at least one
is guaranteed to be“good”.Then,to perform the approx-
power of randomized computation.Examples of hardness vs.randomness tradeoffs based on worst-case circuit com-plexity assumptions may be found in[3,5,6,9],(see also table1).Our contribution is a construction that gives a better tradeoff between the simulation quality and the strength of the assumed lower bound.The improve-ment is especially noticeable for“mid-level”strength func-tions,for example.
1.1.Derandomization via generators
Following[12],the task of derandomizing(two sided er-ror)probabilistic algorithms reduces to the problem of de-terministically approximating the fraction of inputs which a given circuit accepts.De?ne an approximator as a determin-istic algorithm that gets as input a circuit and approximates the fraction of inputs accepted by the circuit.An approx-imator can be used to deterministically simulate a proba-bilistic algorithm on a given input in the obvious way.The running time of the simulation is roughly the running time of the approximator,and our goal becomes constructing ef-?cient(in terms of running time)approximators.Previous Hardness vs.Randomness tradeoffs constructed ef?cient approximators via pseudo-random generators.
A pseudo-random generator is a family of functions
,which is computable in time,and has the property that for every,the set of
all outputs of can be used to approximate1the fraction of inputs accepted by any circuit of size.
Intuitively,the generator“stretches”a short seed of random bits into a long string of pseudo-random bits which“fool”every circuit of size.
A pseudo-random generator is suf?cient to construct an approximator.Simply run the given circuit on all outputs of the pseudo-random generator.Thus the running time of this approximator is exponential in the generator’s seed size. (One has to go over seeds and activate the generator which runs in time).
If one settles for derandomizing one sided error proba-bilistic algorithms,the object in need is a Hitting set.A hitting set(for circuits of size)is a(multi)-set of strings in which has the property that for every circuit of size which accepts at least half of the inputs there exists an element,which accepts.A hitting set genera-tor is a family of functions, which is computable in time,and has the property that for every,the set of all outputs of is a hitting set for circuits of size.Every pseudo-random generator is
bits,and pro-duces bits.
Previous results using worst-case assumptions([3,5,6,
9])focused on“hardness ampli?cation”,that is showing
the-distributional complexity hardness assumption fol-lows from the-worst case circuit complexity assumption
(with some relation between and).Hardness vs.ran-domness tradeoffs then follow using hardness ampli?cation
and activating the NW-generator.Recently,[9]came up
with an almost optimal hardness ampli?cation scheme.In-formally speaking,they show that the two assumptions are
equivalent.More precisely,they show that given a func-
tion that meets the-worst case cir-cuit complexity assumption,one can construct a function which meets the requirements of the-distributional complexity hardness assumption.This is op-
timal for our purposes since we are indifferent to the differ-
ence between and.
Having pushed the hardness ampli?cation phase to the
limit,any remaining inef?ciency in the derandomization
process is caused by the NW-generator.Indeed,there are some inherent limits to the NW-generator that make these derandomization sub-optimal for functions which are not exponential.
When assuming the-worst case circuit complexity as-
sumption,one may hope to get a generator
Table1.Result Comparison
All results assume the-worst-case circuit complexity assumption.
Reference Conclusion for
[3]
[6]a
this paper b
optimal c
a Impagliazzo and Wigderson state their result only for,and their result puts in,for such a lower bound.
b Our result is a bit better,but we cannot state it in this notation.
c The best we can hope for with current techniques,see section6.
that fools circuits of size,(see section6).How-
ever,the best result using the NW-generator takes a larger
seed:
circuits rather than size cir-cuits2.The second loss is that rather than constructing a generator,we construct candidate generators where at least one of them is a“good”generator.We don’t know how to?nd the good generator in the huge collection.Nev-ertheless,a trivial observation is that this collection may be used to construct a hitting set generator.This is because the set of outputs of all candidate generators is a small hit-ting set.This suf?ces for one sided error probabilistic algo-rithms.As for two sided error probabilistic algorithms,we show that this collection of candidate generators is useful to construct an approximator which runs in time,(that
.The main lemma of[7]says that if the NW-generator is not a pseudo-random generator then is easy.More precisely,the statement is that if there exists a circuit of size which“catches”,then there exists a circuit of size which approximates.Put dif-ferently,this means that when used with seed size,the NW-generator can fool circuits of size.To get ,one needs.Since
cuit complexity of some functions on bits.We distinguish between two cases:The optimistic case is that these func-tions can be computed by much smaller circuits.In this case the NW-lemma shows that is a good generator, and we don’t lose the factor.In the second case the pes-simistic bound applies and the NW-construction may fail to produce a correct generator.However,in this case we get a function which is harder than the original one we started with.This puts us in better condition as the NW-generator works better if you have a harder function.
To be more precise,we make the following observation: when used with some function the NW-generator speci?es a family of functions over bits(which are restric-tions of the given function).Either is not a good generator,or one of the speci?ed functions requires large circuit size.[7]choose small enough to ensure that the latter case is impossible using the fact that every function on bits has a circuit of size at most.We instead choose and consider the following two cases:
1.All the functions speci?ed by the NW-generator have
“small”(size)circuits.In such a case we get that the NW generator is indeed good for circuits of size,and we don’t lose the factor.
2.At least one of the speci?ed functions cannot be com-
puted by a circuit of size.In this case it may be that the NW-generator is bad.However,we have at hand a function on many fewer bits than the original hard function(instead of),that requires roughly the same circuit size.From the point of view of the ratio between hardness to input size,this function is harder than the one we started with.We can“plug”it to the NW-generator and enjoy the better lower bound.(Re-call that the NW-generator is more ef?cient with strong lower bounds).This approach can be used recursively until we end up with a function which requires circuits of size exponential in the length of it’s input.On such
a function we can afford to use the old proof.
The construction:While this may seem very appealing, there are still some problems.We don’t know which of the two cases happened,and even worse,in case2,we don’t know which of the speci?ed functions is the hard function.Thus,we try all possibilities.We construct candi-date generators from the initial function and all its speci?ed functions.We continue this recursively until we are sure that one of the functions we consider is“hard”but all its speci?ed functions are“easy”.This can be shown to hap-pen after at most recursion levels,and at this point we have candidates.This process involves some loss3.At this point we know that at least one candidate is a good generator.
.
From here we may continue in two different paths.It is easy to see that the union of outputs of all candidate gener-ators is a hitting set of size for circuits of size.This makes our construction a hitting set generator and suf?ces for one sided error probabilistic algorithms.We are not able to spot the good generator.However,in order to derandom-ize two sided error probabilistic algorithms it is enough to construct an approximator.[1]showed how to construct an ef?cient approximator given a hitting set generator.Our hit-ting set generator has special properties that makes the proof simpler.Recall that to construct an approximator,we need to approximate the fraction of inputs accepted by a given circuit.The idea is to hold a“tournament”between all the candidate generators.The winner of this tournament is not necessarily a good generator.However,it is certain to give a good approximation of the fraction of inputs accepted by the circuit we are given as input.
A disperser a la Trevisan:Recently,Trevisan[11]used the NW-generator to construct an extractor.An extractor is an ef?ciently computable function
,such that for all distributions on having min-entropy4,the distribution obtained by sampling ac-cording to,uniformly from and computing ,is statistically close to the uniform distribution on bits.Trevisan’s extractor works by treating as a func-tion over bits,“amplifying”its hardness,and ap-plying.
We focus our attention to constructing extractors with minimal.Trevisan’s construction suffers from the same inef?ciency of the NW-generator which we treat here.As suggested by our choice of letters,in the extractor terminol-ogy,the min-entropy takes the role of“hardness”.Indeed, Trevisan extractors use bits.As in the case of pseudo-random generators,the optimal5seed size is.We don’t get an improved extractor, since our construction does not give a pseudo random gen-erator.Instead we get the information theoretic analog of a hitting set generator which is called a disperser.The exact de?nition appears on section5.Unlike extractors,there ex-ists an explicit construction of optimal dispersers by[10]. Our construction is slightly inferior to the optimal one,but involves totally different methods.
The technique used in this paper(combined with some more ideas)can be used to construct an almost optimal pseudo-random generator and therefore an almost optimal extractor.We delay this to a future paper.
https://www.doczj.com/doc/359850529.html,anization of the paper
Section2includes de?nitions and cites the previous re-sults needed for our construction.Section3includes the main theorem and the main construction.Section4shows how to use the main construction to give an approximator. Section5concerns using Trevisan’s method with our result and constructs a disperser.Section6includes a construction of hard functions from hitting set generators and explains what we mean by optimal derandomization.
2.De?nitions and History
2.1.Hard functions
We start by de?ning“hardness”in both worst-case com-plexity and distributional complexity settings.
De?nition1For a function,we de-?ne:
1.circuits that compute cor-
rectly on every input
2.cir-
cuits of size
3.
When invoking the NW-generator against circuits of size ,one needs a function,with
2.2.Generators,Discrepancy sets,Hitting sets and
Approximators
In this section we de?ne pseudo-random/hitting set gen-erators,and algorithms we call approximators.In the fol-lowing de?nitions we will use the same parameter,both for the size of the circuit,and the size of the input given to the circuit.A circuit of size takes as input at most bits, and in case the circuit takes less bits we assume it takes a pre?x of the bits that we prepare in advance.De?nition2For a circuit of size bits de?ne:
De?nition3A-discrepancy set,is a multi-set ,such that for all circuits of size:
De?nition4A-hitting set,is a multi-set, such that for all circuits of size,if then there exists such that.
An easy observation is that a discrepancy set is also a hitting set,while the converse need not be true.We pro-ceed and de?ne pseudo-random/hitting set generators.In both cases,for the purpose of derandomizing probabilistic algorithms,generators may be allowed to run in time expo-nential in their input.
De?nition5A-pseudo-random generator(resp.-hitting set generator)is a family of functions
,such that:
1.For all,the set
is a-discrepancy set(resp.-hitting set).
2.is computable in time,(exponential in the size
of the input).
From now on we will drop the“pseudo-random”when talk-ing about pseudo-random generators.
The existence of“good”generators implies a non-trivial deterministic simulation of two sided error probabilistic al-gorithms.However,the proof works by building the follow-ing device(which appears implicitly since[12]and is also used implicitly in other efforts to derandomize such as[1]).
De?nition6A-approximator is a deterministic algorithm that takes as input a circuit and outputs an approximation of,that is,a number such that
The following two implications are standard:
Lemma1([12])
1.If there exists a-generator
,then there exists a-approximator that(on
a circuit of size)runs in time.
2.If there exists a-approximator that runs in
time on circuits of size,then
.
Proof:(sketch)
Having a generator,one can run the given circuit on all possible outputs of the generator.This is indeed an ef-?cient approximator.Having an approximator,and given a probabilistic algorithm,(where is the input, and is the random string),simply construct the circuit
,and approximate it’s success probabil-ity.
As seen from lemma1,the task of derandomizing two sided error probabilistic algorithms,reduces to construct-ing ef?cient generators,(Where ef?ciency means small as possible seed size).A similar argument shows that ef?cient hitting set generators suf?ce for one sided error probabilis-tic algorithms.
2.3.The NW-generator
In this section we present the NW-generator,it’s best known consequences for derandomization,and explain it’s inherent inef?ciency when used with a sub-exponential lower bound.
Theorem2[7](Construction of nearly disjoint sets)There exists an algorithm that given numbers,such that
,for some constant.
3.The running time of the algorithm is exponential in.
De?nition7(The NW-generator[7])Given some function ,and,the NW-generator works by building an-design,.It takes as input bits,and outputs bits.
The thing to do now,is prove that if one“plugs”a hard enough into the NW-generator,it fools circuits of some size.
Lemma2[7]Fix,and
,be the promised bound on the intersection size.Let
,be a function such that
.
The drawback in lemma2,is that
,must be increased to roughly
,which fools circuits of size,for.
We may still expect to have an optimal generator,that is
which fools circuits of size .This cannot be achieved by improving the design, as the next lemma shows that the current construction of designs is optimal.
Lemma3If,and for all,
,and for all,,and Proof:It is enough to prove the lemma for
runs in time,with
For more general functions,the above equation does not seem to have a nice closed-form solution.How-ever,since the derandomization takes time,we can pick Then since,
.This gives:
Theorem5Let be a function computable in time ,such that for all,,then
.
This approximator should be compared to the one of[9], which is constructed by applying theorem3and lemma1 in sequence.[9]’s approximator runs in time
.Let be the-design promised by theorem2,and let
.Consider the set:
If is not a-discrepancy set then there exist some ,and a partial assignment,such that:
.This matches the assumption about.None of the restricted functions can require circuit complexity
circuits which com-pute for https://www.doczj.com/doc/359850529.html,bining these with ,and using(1),we get a circuit of size
1.is computable in time.
2.For all,.
Parameters for the construction:
-the seed length.
-the length of the“pseudo-random”string,
(which is also the size of the circuit
we want to fool).
-a bound on the error of the generator.
-an input length on which is hard.
-the lower bound known on,
(that is a number such that:).
The construction works by recursively calling the procedure construct(),(where are integers and is a function from to,represented as a truth table).(and are also inputs,but left unchanged in recursive calls.)The?rst call is to construct()
construct()
https://www.doczj.com/doc/359850529.html,e theorem1to create a function
,such that if,then
).
https://www.doczj.com/doc/359850529.html,e theorem2to create a,.
3.Let
,return.
6.For all,and for all,Call
construct(
.
4.
.
Claim2The process described can be performed in time .
Proof:We have already?xed.The work done in each instantiation of construct can be done in time
.We will bound the size of the recursion tree.The degree of the recursion tree at level is bounded https://www.doczj.com/doc/359850529.html,ing the fact that for all,
.This means that the total number of in-stantiations is bounded by
Claim3The depth of the recursion tree is bounded by .
Proof:We simply have to estimate such that
.Using lemma4, we get that if the produced at the current instantiation of construct is not a-discrepancy set,then there exists a restriction,(for some choice of),such that:
tree.From claim4we get that if non of the’s in levels up to is a-discrepancy set then one of the’s in the last level has.And so,
-discrepancy https://www.doczj.com/doc/359850529.html,-ing the fact that at level,
6Actually,[1]constructs an approximator from a hitting set generator. Thus we could be done stating this result.However,we can give a much simpler proof for this particular setup.and picks a row such that all the numbers in
lie on an interval of length.It then returns,the middle of the interval of.Such an exists,because has that property.For all,we have that,and therefore all the numbers in,are at a distance of at most from.From this we have that.
By applying theorems6,7in sequence we get the approx-imator,and prove theorem4.
5.An information theoretic analog a-la Tre-
visan
Recently,Trevisan[11]used the NW-generator to con-struct an extractor.Trevisan’s extractor suffers from the same inef?ciency of the NW-generator.In this section we use our technique to build a disperser.
De?nition9A function
is called a-disperser,if for any,such that ,,(where denotes the set of elements in such that there exists and such that.
Unlike extractors,dispersers with small seed size have already been constructed by[10].Our construction achieves a totally different almost optimal disperser.
Theorem8For every there exists an-disperser,where
-hitting set generator ,where,then there ex-ists a function,where, such that:
1.is in.
2.For all,.
Proof:The function is de?ned as follows:On input of size,construct the hitting set
.Accept if it does not appear as a pre?x of an element of.One can easily compute by con-structing,(which takes time),and comparing the given input to all strings in.This puts in.Since ,if one looks at the?rst bits of elements in ,he will encounter at most half of the possible pat-terns.This means that accepts at least half of the inputs in.If is computed by some circuit of size ,then accepts half of the possible inputs in. We may view as a circuit that takes inputs and ig-nores all but of them.Since is a hitting set,then there must be an element in which accepts,but this contra-dicts the de?nition of.
As a consequence we get that any construction that was superior to that we list as“optimal”7,would be a proof that circuit lower bounds for functions in could be au-tomatically boosted to give even larger lower bounds.This would show a gap in the possible circuit complexities of functions complete for.Since proving such a gap seems quite dif?cult,we believe progress in derandomization by current techniques is limited to matching the”optimal”bounds listed.Putting this in terms of simulation of prob-abilistic algorithms,if we start from a function with lower bound,the best result we can hope for is
.
Acknowledgements
We thank Oded Goldreich for a conversation8that started us working on this paper,and for many helpful comments. We thank Ido Bregman for reading a preliminary version of this paper and for helpful comments.
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英语常见构词法 一、常见的前缀 前缀一般会改变词义，但不改变词性；后缀一般不改变词义，而不改变词性。1.表示否定意义的前缀 1)纯否定前缀 a-, an-, asymmetry（不对称）anhydrous（无水的） dis- dishonest, dislike I类：in-, ig-, il, im, ir, Incapable（不能的，无能力的，不能胜任的）, inability（无能力，无才能）, Ignoble（不光彩的，卑鄙的，卑贱的）, impossible, immoral（不道德的）, illegal（不合法的）, irregular（不规则的） ne-, n-, none, neither（either两者中一个）, never non-, nonsense（胡说，废话；荒谬的）sense（感觉，观念，道理）neg-, neglect（疏忽，忽视） un- unable, unemployment（失业） 2)表示错误的意义 male-, mal-, malfunction（发生故障，不起作用；故障）, maladjustment(失调) mis-, mistake, mislead（误导，带错） pseudo-, pseudonym(假名), pseudoscience 注：pseudo（伪君子，假冒的） 3)表示反动作的意思 de-, defend（辩护，防护）, demodulation(解调) dis-, disarm（裁军，解除武装）, disconnect（拆开，使分离，断开） discover = uncover发现
re-，reverse反面的，反转，倒车 un-, unload（卸载，卸，卸货）, uncover（发现，揭开） with-, withdraw（stop sth or stop making sth撤退，撤消，取款）, withstand（抵挡，反抗，经得起，。。。站立不倒be strong enough not to be harm） withhold(阻止，。。。抓住不放to refuse to give sth to someone) 4)表示相反，相互对立意思 anti-, ant- antiknock( 防震), antiforeign,(排外的) contra-, contre-, contro-, contradiction（矛盾，否认，反驳）, contro-flow(逆流) counter-, counterreaction（抵抗，发对的行动，中和）, counterbalance（使平衡，自动抵消） O类（可以不记忆） ob-, oc-, of-, op-, object（物体；反对，拒绝）, oppose, occupy 2. 表示空间位置，方向关系的前缀 1)a- 表示“在……之上”，“向……”（与空间类名次搭配） aboard, aside, 2)by- 表示“附近，邻近，边侧” bypath（侧道，小路）, bypass(弯路) 3)circum-, circu-, 表示“周围，环绕，回转” circumstance（环境，情况）, circuit（电路，回路） 4)de-, 表示“在下，向下” descend（下降；沿。。。向下）, degrade（使降级，贬低；降级；grade 年级，成绩，级别） 5)en-, 表示“在内，进入”（不记忆） encage（关在笼中，禁闭）, enbed(上床) 6)ex-, ec-, es-, 表示“外部，外”
翻译研究中的概念混淆 ——以“翻译策略”、“翻译方法”和“翻译技巧”为例 熊兵华中师范大学 《中国翻译》2014（3）82-88 摘要：本文对学界在“翻译策略”、“翻译方法”和“翻译技巧”这三个基本概念上所存在的普遍的混淆进行了剖析，提出应对这三个概念进行明确区分。在此基础上论文对这三个概念的定义、特性、相互关系及其各自的分类体系进行了系统阐述。 关键词：翻译策略；翻译方法；翻译技巧；混淆；定义；分类 1．话题缘起 在翻译研究中，有一个问题一直以来都未引起学界足够的重视，并因此在一定程度上妨碍了翻译研究的进一步发展，这个问题即为翻译研究中的概念混淆，其中又尤以“翻译策略”、“翻译方法”和“翻译技巧”这三个概念的混淆为甚。一方面，学界对翻译“策略”、“方法”和“技巧”的讨论虽多如牛毛，但把它们作为一个方法论系统的关键要素进行综合研究，深入考察其各自的内涵、相互关系及分类体系的系统性研究还相当少见。另一方面，学界在对这三个术语的认识和使用上普遍存在着定义不明、分类不当、概念混淆不清的问题。例如，在一些翻译教材中中，“归化”与“异化”一方面被作为“翻译方法”加以讨论(如龚芬，2011：79—81)，另一方面又被视为“翻译策略”进行阐述(同上：93—106)。一些翻译论文把本应属于翻译技巧层面的增补型翻译(类似于增译)、浓缩型翻译(类似于减译)划归为“翻译策略”的类别(如李克兴，2004：66—67)。在一些翻译方向的硕士研究生论文中，把翻译“策略”、“方法”、“技巧”混为一谈的更是比比皆是。甚至翻译专业的老师对此问题也存在一些模糊的、甚至是错误的看法(如把归化等同于意译，把异化等同于直译)。 国外学界对此也存在一些模糊或混淆(或未予严格区分。比如Shuttleworth & Cowie (2004：44，59)一方面把domestication／foreignization称作是“strategy”，另一方面却又把free／literal translation也视为“strategy”(同上：63，96 )。Vinay & Darbelnet (1958／2000：84—93) 把翻译方法(method)分为两类：直接翻译(direct translation)和间接翻译(oblique translation)，前者包括三种处理方式(procedures)，即借译，拟译，直译，后者包括四种处理方式，即词类转换，视点转换，等值翻译，顺应翻译。可在论述中却经常把其划分出来的“方法”(methods)和“处理方式”(procedures)混为一谈。另外，他们把“借译、拟译、直译、等值翻译、顺应翻译”和“词类转换、视角转换”划归为一类(同属procedures)也欠妥当，因为前者应属“翻译方法”的范畴，而后者则应属“翻译技巧”的范畴。实际上，这里Vinay ＆Darbelnet的分类涉及到三个层面：翻译策略、翻译方法和翻译技巧。其划分出来的两大“翻译方法”(直接翻译和间接翻译)其实应为“翻译策略”(所以Munday说，“The two general translation strategies identified by Vinay ＆Darbelnet are direct translation and oblique translation”，见Munday，2008：56)，而在其划分出来的七类“处理方式”中，前三类和最后两类属于“翻译方法”，第四、第五类则属于“翻译技巧”。总之，Vinay ＆Darbelnet在其分类中把翻译“策略、方法、技巧”混淆在一起，这也导致后来很长时间学界在这几个概念上的混淆(Molina ＆Albir，2002：506 0 关于国外译学界在译学术语，特别是在“翻译策略、翻译方法、翻译技巧”三个术语上所存在的概念混淆、使用混乱的问题，Chesterman(2005)和Molina & Albir(2002)曾专门撰文予以讨论。如Chesterman指出，学界用于描述文本操作过程的术语除翻译“策略”外，其他还有“技巧、方法、转换、转化、变易”等等(2005：17)。他认为这种众多术语相互混用
英语构词通常包括六种方法：转化法、派生法、合成法、混合法、截短法和首尾字母结合法。 一、【派生法】 英语构词法中在词根前面加前缀或在词根后面加后缀，从而构成一个与原单词意义相近或截然相反的新词的方法叫作派生法。 1.前缀 除少数英语前缀外，前缀一般改变单词的意义，不改变词性；英语后缀一般改变词类，而不引起词义的变化。 (1)表示否定意义的前缀常用的有dis-, il-, im-, in-, ir-, mis-, non-, un-等，在单词的前面加这类前缀常构成与该词意义相反的新词。例如： agree同意→disagree不同意 fair公平的→unfair不公平的 possible可能的→impossible不可能的 understand理解→misunderstand误解 (2)表示其他意义的前缀常用的有a-(多构成表语形容词), anti- (反对；抵抗), auto- (自动), co- (共同), en- (使), inter- (互相), re- (再；又), sub- (下面的；次；小), tele- (强调距离)等。例如： co-worker 同事，帮手 enlarge 使变大 cooperate 合作 rewrite 重写
subway 地铁 2.后缀 给单词加后缀也是英语构词的一种重要方法。后缀通常会改变单词的词性，构成意义相近的其他词性；少数后缀还会改变词义，变为与原来词义相反的新词。下面仅作简单介绍。 (1)构成名词的后缀常用的有-ence，-(e)r/ -or (从事某事的人),-ese (某地人)，-ess (雌性)，-ian (精通……的人)，-ist (专业人员)，-ment (性质；状态)，-ness (性质；状态)，-tion(动作；过程)等。例如： differ不同于→difference区别 write写→writer作家 China中国→Chinese中国人 act表演→actress女演员 music音乐→musician音乐家 (2)构成动词的后缀常用的有-(e)n (多用于形容词之后)，-fy (使……化)，-ize (使……成为)。例如： wide→widen加宽 beauty→beautify美化 pure→purify提纯 real→realize意识到 organ→organize组织 sharp→sharpen使变锋利 (3)构成形容词的后缀常用的有-al，-able (有能力的)，-(a)n(某国人的)，-en (多
初中复习资料 目录英语词组总结for 和1.比较since 的四种用法2.since 延续动词与瞬间动词3. 重点部分提要词汇一. 单词⑴ 2冠词a / an / the: 3.some和any 4.family 5. little的用法 三. 语法 1. 名词所有格 2. 祈使句 1.英语构词法汇 2.英语语法汇总及练习 第1讲：名词 第2讲：代词 第3讲：形容词
第4讲：副词 第5讲：动词 第6讲：不定式 第7讲介词 第8讲：连词 第9讲：时态一 第10讲：时态(二) 第11讲：动词语态 第12讲：句子种类（一） 第13讲：句子的种类(二) 讲：宾语从句14第 第15讲：状语从句There be句型与中考试题第17讲ABC 被动语态复习第18讲 【初中英语词组总结】1 (see 、hear 、notice 、find 、feel 、listen to 、look at (感官动词)+do eg:I like watching monkeys jump 2 （比较级and 比较级）表示越来越怎么样 3 a piece of cake =easy 小菜一碟（容易）
4 agree with sb 赞成某人 5 all kinds of 各种各样 a kind of 一样 6 all over the world = the whole world 整个世界 7 along with同……一道，伴随……eg : I will go along with you我将和你一起去 the students planted trees along with their teachers 学生同老师们一起种树 8 As soon as 一怎么样就怎么样 9 as you can see 你是知道的 10 ask for ……求助向…要…（直接接想要的东西） 11 ask sb for sth 向某人什么 12 ask sb to do sth 询问某人某事ask sb not to do 叫某人不要做某事 13 at the age of 在……岁时 14 at the beginning of …………的起初；……的开始 15 at the end of +地点/+时间最后；尽头；末尾 16 at this time of year 在每年的这个时候 17 be /feel confident of sth /that clause +从句感觉/对什么有信心，自信 18 be + doing 表：1 现在进行时2 将来时 19 be able to (+ v 原) = can (+ v 原）能够…… 21 be afraid to do (of sth 恐惧，害怕……
学点构词法（对扩大词汇量很有帮助喔＾＾加油） 一. 常见的前缀 1.表示否定意义的前缀 1)纯否定前缀 a-, an-, asymmetry（不对称）anhydrous（无水的） dis-, dishonest, dislike in-, ig-, il-, im-, ir-, incapable（无能力的、不胜任的）, inability（无能）, ignoble（平民的、卑贱的）, impossible, immoral（不道德的）, illegal（非法的、非法移民）, irregular（不规则的、不合法的） ne-, n-, none, neither, never non-, nonsense neg-, neglect（忽略） un- unable, unemployment（失业） 2)表示错误的意义 male-, mal-, malfunction（故障、发生故障的）, maladjustment(失调) mis-, mistake, mislead（误导） pseudo-, pseudonym(假名), pseudoscience（伪科学） 3)表示反动作的意思 de-, defend（防护、防守、辩护）, demodulation(解调) dis-, disarm（裁军、解除武装、缓和）, disconnect（使分开，拆开） un-, unload, uncover 4)表示相反，相互对立意思 anti-, ant-, antiknock(防震), antiforeign,(排外的) contra-, contre-, contro-, contradiction（矛盾、不一致）, contraflow(逆流) counter-, counterreaction(逆反应), counterbalance（平衡、使抵消） ob-, oc-, of-, op-, object, oppose, occupy with-, withdraw（撤回、撤退）, withstand（对抗） 2. 表示空间位置，方向关系的前缀 1)a- 表示“在……之上”，“向……” aboard, aside, 2)by- 表示“附近，邻近，边侧” Bypath（侧道）, bypass(弯路) 3)circum-, circu-, 表示“周围，环绕，回转” Circumstance（环境）, circuit（巡回、绕路） 4)de-, 表示“在下，向下” Descend（下降、突然拜访）, degrade（降格，使屈辱） 5)en-, 表示“在内，进入” Encage（把……关起来） 6)ex-, ec-, es-, 表示“外部，外” Exit（出口、离去）, eclipse（使……黯然失色）, expand（扩张）, export（出口） 7)extra-, 表示“额外”
s 初中英语构词法汇总及练习 一。概念 英语的构词法主要有：转化法，合成法，派生法，混成法，截短法和词首字母缩略法。 二。相关知识点精讲 1.转化法 英语中，有的名词可作动词，有的形容词可作副词或动词，这种把一种词性用作另一种词性而词形不变的方法叫作转化法。 1)动词转化为名词 很多动词可以转化为名词，大多意思没有多大的变化(如下①)；有时意思有一定变化(如下②)；有的与一个动词和不定冠词构成短语，表示一个动作(如下③)。例如： ① Let's go out for a walk. ______________________________ ②He is a man of strong build 。______________________________ 2)名词转化为动词 很多表示物件(如下①)、身体部位(如下②)、某类人(如下③)的名词可以用作动词来表示动作，某些抽象名词(如下④)也可作动词。例如： ① Did you book a seat on the plane? _____________________________? ②Please hand me the book 。____________________________________。 ③She nursed her husband back to health 。______________________________。 ④We lunched together 。__________________________________。 3)形容词转化为动词 有少数形容词可以转化为动词。例如： We will try our best to better our life 。___________________________________。 4)副词转化为动词 有少数副词可以转化为动词。例如： Murder will out 。(谚语)恶事终必将败露。 5)形容词转化为名词 表示颜色的形容词常可转化为名词(如下①)；某些形容词如old, young, poor, rich, wounded, injured 等与the 连用，表示一类人，作主语时，谓语用复数(如下②)。例如： People should be dressed in black at the funeral 。______________________________。 The old in our village are living a happy life 。__________________________________。 2.派生法 在词根前面加前缀或在词根后面加后缀构成一个与原单词意义相近或截然相反的新词叫作派生法。 1)前缀 除少数前缀外，前缀一般改变单词的意义，不改变词性；后缀一般改变词类，而不引起词义的变化。 (1)表示否定意义的前缀常用的有dis-, il-, im-, in-, ir-, mis-, non-, un-等，在单词的前面加这类前缀常构成与该词意义相反的新词。例如： important 重要的→___important appear 出现→____appear 消失 correct 正确的→___correct 不正确的 lead 带领→___lead 领错 (2)表示其他意义的前缀常用的有a-(多构成表语形容词)， anti- (反对；抵抗)， auto- (自动)， co- (共同)， en- (使)， inter- (互相)， re- (再；又)， sub- (下面的；次；小)，
英语语法大全 初中英语语法学习提纲 一、词类、句子成分和构词法： 1、词类：英语词类分十种： 名词、形容词、代词、数词、冠词、动词、副词、介词、连词、感叹词。 1、名词(n.)：表示人、事物、地点或抽象概念的名称。如：boy, morning, bag, ball, class, orange. 2、代词(pron.)：主要用来代替名词。如：who, she, you, it . 3、形容词(adj..)：表示人或事物的性质或特征。如：good, right, white, orange . 4、数词(num.)：表示数目或事物的顺序。如：one, two, three, first, second, third, fourth. 5、动词(v.)：表示动作或状态。如：am, is,are,have,see . 6、副词(adv.)：修饰动词、形容词或其他副词,说明时间、地点、程度等如：now, very, here, often, quietly, slowly. 7、冠词(art..)：用在名词前,帮助说明名词。如：a, an, the. 8、介词(prep.)：表示它后面的名词或代词与其他句子成分的关系。如in, on, from, above, behind. 9、连词(conj.)：用来连接词、短语或句子。如and, but, before .
10、感叹词(interj..)表示喜、怒、哀、乐等感情。如：oh, well, hi, hello. 2、句子成分：英语句子成分分为七种：主语、谓语、宾语、定语、状语、表语、宾语补足语。 1、主语是句子所要说的人或事物,回答是“谁”或者“什么”。通常用名词或代词担任。如：I’m Miss Green.(我是格林小姐) 2、谓语动词说明主语的动作或状态,回答“做(什么)”。主要由动词担任。如：Jack cleans the room every day. (杰克每天打扫房间) 3、表语在系动词之后,说明主语的身份或特征,回答是“什么”或者“怎么样”。通常由名词、代词或形容词担任。如：My name is Ping ping .(我的名字叫萍萍) 4、宾语表示及物动词的对象或结果,回答做的是“什么”。通常由名词或代词担任。 如：He can spell the word.(他能拼这个词) 有些及物动词带有两个宾语,一个指物,一个指人。指物的叫直接宾语,指人的叫间接宾语。间接宾语一般放在直接宾语的前面。如：He wrote me a letter . (他给我写了一封信) 有时可把介词to或for加在间接宾语前构成短语,放在直接宾语后面,来强调间接宾语。如：He wrote a letter to me . (他给我写了一封信) 5、定语修饰名词或代词,通常由形容词、代词、数词等担任。如： Shanghai is a big city .(上海是个大城市)
外教一对一https://www.doczj.com/doc/359850529.html, 英语构词法：合成法 合成法也是英语构词法中的重要方法，所谓合成法就是将两个或者多个独立的词语连接在一起组成新词的方法。合成法也可以叫复合法，同样地，通过合成法组成的新词又叫做复合词。 那么，复合词的词性怎样来确定呢?很简单： 1.如果两个词语词性一致，那么复合词的词性不变; water(名词)+melon(名词)=watermelon(名词) news(名词)+paper(名词)=newspaper(名词) 2.如果词语词性不同，复合词的词性与最后一个词保持一致; water(名词)+proof(形容词)=waterproof(形容词) white(形容词)+wash(动词)=whitewash(动词) 3.如果介词和其他词汇组合，那么复合词词性要归属其他词汇。 under(介词)+take(动词)=undertake(动词) hanger(名词)+on(介词)=hanger-on(名词) 复合词的词义与原来的词语词义有什么关系呢?很多人会想到，复合词的词义就是把原来的词语词义加起来就可以了。其实不是这样的。复合词的词义不一定是词义之和，而是引申出了新的意义。 Bigwig是由big和wig组合而成的复合词，如果按照词义简单相加的方法来推测词义的话，bigwig的意思是“大假发”或者是“大头脑”，显然不正确。其实这个单词的语义与历史有一定的联系。因为在17世纪到18世纪的时候，英国戴假发是需要依据社会等级和职业的不同而有一定的不同的。往往那些显赫的人物戴假发比普通人戴的要大得多，所以“bigwig”这个词渐渐地流行起来，专指重要人物，其词义被赋予了全新的含义。 复合词数量最多的是复合名词、复合形容词和复合动词。 (1)复合名词 class+room=classroom教室 neck+lace=necklace项链 black+board=blackboard黑板 dead+line=deadline最后期限
英语专题讲座（一） 词汇的分类和构词法 一、复习要点阐述 我们学习的语言的每一篇文章都是由句子构成的。每一个句子都是由或多或少的词构成的。单词是语言构成的最基础内容，教英语的老师通常会说“一个学生记住的词汇是与他的英语成绩成正比。”这句话说明了一件事，就是词汇量的重要性。就像我们自己的国语中文，一个孩子从小到大，在日常生活中记住了大量的词汇，所以运用起来很自如。如何能记好英语的单词及其他们的用法，了解英语的词类和构词法对于一个考生来说是很重要的。所以在今天的专题中，我们将复习英语的词类和初中阶段我们所要掌握的几种构词法，使同学们对英语的词类及其简单的用法有一定的了解，并能正确的使用词汇。 二、要点复习的策略及技巧 （一）英语的词类 英语中的词类根据其语法功能分为名词、冠词、代词、数词、副词、介词、连词、感叹词、动词十类。根据意义又可以分为实词和虚词。实词指具有实际意义并能单独作句子成分的词。这些词是名词、数词、代词、形容词、副词和动词六类。虚词指没有实际意义或实际意义不明显、不能在句子中单独作句子成分的词。这些词是冠词、介词、连词和感叹词四类。 下面我们就词汇的分类、名称、作用及例词列表。
从上面的表格中我们简要地讲解了英语十大词类的基本用法。同学们可以仔细的体会一下此表的内容。这将有助于你们对句子和文章的理解。 （二）构词法 语言的基本要素之一是词汇。在语言发展的最初阶段，人们使用的是少量而简单的词汇，这些词汇只表示日常简单的事物和概念，构成语言最基本的词，称为原生词，也叫基本词或词根词。英语里的原生词大都是单音节的。如：sun, man, head, foot, fish, see, run等，其数量是有限的。随着社会的发展与进步，语言的扩充与融合使语言变得复杂，原有的原生词已不够用，人们便创造了一些新词来表示新有的事物与概念，按照语言一定的规律创造新词的方法，就是我们所说的构词法。在初中阶段我们学习了下列构词法，了解了这些构词法，我们对词的用法就更方便一些。 1. 合成法 合成法至今保持着旺盛的生命力，在现代英语中不少新词都是借助原有的词合成的。 1)名词+名词→名词 basket(篮子)+ball(球)→basketball篮球book (书) +shop(商店) → bookshop书店 book(书) + store(商店) → bookstore书店house(房子) +work(劳动) → housework家务劳动 home(家庭) + work(工作) → homework家庭作业 2) 形容词+名词→名词 black(黑色的)+board(木板)→ blackboard黑板English(英国的)+man(人)→ Englishman英国人 loud(大声的)+speaker(说话者) → loudspeaker扬声器 3)动词的-ing形式＋名词
四种翻译方法 1.直译和意译 所谓直译，就是在译文语言条件许可时，在译文中既保持原文的内容，又保持原文的形式——特别指保持原文的比喻、形象和民族、地方色彩等。 每一个民族语言都有它自己的词汇、句法结构和表达方法。当原文的思想内容与译文的表达形式有矛盾不宜采用直译法处理时，就应采用意译法。意译要求译文能正确表达原文的内容，但可以不拘泥与原文的形式。（张培基） 应当指出，在再能确切的表达原作思想内容和不违背译文语言规范的条件下，直译有其可取之处，一方面有助于保存原著的格调，另一方面可以进新鲜的表达方法。 Literal translation refers to an adequate representation of the original. When the original coincides or almost tallies with the Chinese language in the sequence of vocabulary, in grammatical structure and rhetorical device, literal translation must be used. Free translation is also called liberal translation, which does not adhere strictly to the form or word order of the original.（郭著章） 直译法是指在不违背英语文化的前提下，在英译文中完全保留汉语词语的指称意义，求得内容和形式相符的方法。 意译是指译者在受到译语社会文化差异的局限时，不得不舍弃原文的字面意义，以求疑问与原文的内容相符和主要语言功能的相似。（陈
高中英语知识点：构词法知识点 一.概念 英语的构词法主要有：合成法，转化法，派生法，混成法，截短法和词首字母缩略法。 二.相关知识点精讲 1.转化法 英语中，有的名词可作动词，有的形容词可作副词或动词，这种把一种词性用作另一种词性而词形不变的方法叫作转化法。 1)动词转化为名词 很多动词能够转化为名词，大多意思没有多大的变化(如下①);有时意思有一定变化(如下②);有的与一个动词和不定冠词构成短语，表示一个动作(如下③)。例如： ①Let's go out for a walk.我们到外面去散散步吧。 ②He is a man of strong build.他是一个体格健壮的汉子。 ③Let's have a swim.咱们游泳吧。 2)名词转化为动词 很多表示物件(如下①)、身体部位(如下②)、某类人(如下③)的名词能够用作动词来表示动作，某些抽象名词(如下④)也可作动词。例如： ①Did you book a seat on the plane?你订好飞机座位了吗? ②Please hand me the book.请把那本书递给我。 ③She nursed her husband back to health.她看护丈夫，使他恢复了健康。 ④We lunched together.我们在一起吃了午餐。 3)形容词转化为动词 有少数形容词能够转化为动词。例如： We will try our best to better our living conditions.我们要尽力改善我们的生活状况。 4)副词转化为动词
有少数副词能够转化为动词。例如： Murder will out.(谚语)恶事终必将败露。 5)形容词转化为名词 表示颜色的形容词常可转化为名词(如下①);某些形容词如old， young， poor， rich，wounded， injured等与the连用，表示一类人，作主语时，谓语用复数(如下②)。例如： You should be dressed in black at the funeral.你在葬礼中该穿黑色衣服。 The old in our village are living a happy life.我们村的老年人过着幸福的生活。 2.派生法 在词根前面加前缀或在词根后面加后缀构成一个与原单词意义相近或截然相反的新词叫作派生法。 1)前缀 除少数前缀外，前缀一般改变单词的意义，不改变词性;后缀一般改变词类，而不引起词义的变化。 (1)表示否定意义的前缀常用的有dis-， il-， im-， in-， ir-， mis-， non-， un-等，在单词的前面加这类前缀常构成与该词意义相反的新词。例如： appear出现→disappear消失 correct准确的→incorrect不准确的 lead带领→mislead领错 stop停下→non-stop不停 (2)表示其他意义的前缀常用的有a-(多构成表语形容词)， anti- (反对;抵抗)，auto- (自动)， co- (共同)， en- (使)， inter- (互相)， re- (再;又)， sub- (下面的;次;小)， tele- (强调距离)等。例如： alone单独的anti-gas防毒气的 Auto-chart自动图表 cooperate合作enjoy使高兴 internet互联网reuse再用
初中英语构词法大全 英语构词方法主要有三种：即转化、派生和合成。 一. 转化 英语单词的词性非常活跃，名词用作动词，动词转化为名词，形容词用作动词等现象非常普遍，这种把一种词性用作另一种词性的方式就叫做词性的转化。 1. 动词和名词之间的相互转化。有时意思变化不大，有时有一定的变化。 1) 动词转化为名词。如： I often go there for a walk. 我经常去那里散步。(句中walk由动词转化为名词) 2) 名词转化为动词。如： Have you booked your ticket?你的票订好了吗? (句中book由名词转化为动词,词意引申为“预订”) Hand me your knife, please. 请把你的刀子递给我。(句中hand由名词转化为动词,词意引申为“传递”) 2. 少部分形容词转化为动词。 The train slowed down to half its speed. 火车速度减慢了一半。(句中slow由形容词转化为动词，词意引申为“减速”) 3. 形容词和名词之间的相互转化。 The poor were not allowed to go into this park those days. 那个时候，穷人是不允许进入这个公园的。 (句中poor由形容词转化为名词，词意引申为“穷人”) 4. 有些词词形不变，只因词尾的清浊音变化而发生词类转化，有时词形也可以变化。如: excuse [?ks?kju?z]v.原谅excuse [?ks?kju?s]n.原谅 use [ju?z]v.用use [ju?s]n.用 二. 派生 派生词是在一个单词前面加前缀或后面加后缀构成新词。 1. 通过加前缀构成另一个词。 前缀一般不造成词类的转换，但能引起词义的变化。前缀中有相当一部分可构成反义词。常用的前缀有： 前缀意义例词 dis- 不,相反的dislike, disappear, dishonest im- 不impossible, impolite mis- 错误的misunderstand re- 重新,再次rewrite, retell super- 超级supermarket, superman un- 不unfair, unhappy, unable 个别前缀也可以引起词类的变化，如： en-可以和名词或形容词构成动词：enable（使能够）, encourage（鼓励）； a-可以和名词构成形容词：asleep（睡着的）； 2. 通过加后缀构成另一个词。后缀不仅能改变词义,也能改变词类。 (1) 常用的构成名词的后缀： 后缀意义例词 -er 人,动作者worker, singer, recorder, teacher -or 人,动作者actor, visitor, editor,inventor,director -ist 人chemist, dentist, scientist,artist -ess 女性actress, waitress -ment 行为,动作government, movement, development -ness 状态,性质illness, happiness,sadness -ion 动作,状态discussion, decision, organization -th 状态length, wealth, truth -ese 人Japanese, Chinese -ian 人American, musician, historian -ship 状态friendship
重点专题构词法归纳 1．派生法 （1）前缀 ①表示否定意义的前缀： a. 纯否定前缀： un-: unable, unemployment（失业）, unload（卸载）, uncover（揭开）, unhappy, untrue, unlike （不像）, unrest（不安的，动荡的）, unfair, unknown, unhealthy, unusual, uncertain, unclear（不清楚的）, unequal, unlucky, unreal, unkind, uncomfortable, uneasy（心情不安的）, uninteresting, unimportant, unnecessary, unpleasant, undivided, unreserved（无保留的） dis-: dislike（不喜欢）, disarm, disconnect, disagree, disappear, disadvantage, dishonest, disability, discover（发现）, disobey in-, im-, il-, ir-: incapable, inability, incomplete, incorrect, inconvenient, inexpensive, impossible, immoral（不道德的）, illegal（非法的）, illogical（不合乎逻辑的）, irregular, irrelative non-: non-smoker, non-stop, non-violent（非暴力的）, nonwhite, non-member, nonparty（无党派）, nonsense（无意义） b. 表示错误的意义： mis-: mistake, mislead（误导）, misunderstanding, misuse, mis-spell, mistrust, mistreat c. 表示“反、防、抗”的意义： anti-: antiknock（防震）, antiforeign（排外的）, anti-war, antitank（反战车的）, anti-pollution ②表示空间位置、方向关系的前缀： a- 表示“在……之上”，“向……”：aboard, aside de- 表示“在下，向下”：decrease（下降）, degrade en- 表示“在内，进入”：encage（入笼）, enbed（上床） ex- 表示“外部，外”：exit, expand（扩张）, export fore- 表示“在前面”：forehead, foreground, foreleg, forefoot in-, im- 表示“向内，在内，背于”：inland, inside, indoor(s), import inter- 表示“在……间，相互”：international, interaction, internet, interview mid- 表示“中，中间”：midposition out- 表示“在外部，在外”：outline, outside, outward(s), outdoor(s) over- 表示“在上面，在外部，向上”：overlook, overhead, overboard, overcoat, overdress, oversea(s)(海外) post- 表示“向后，在后边，次”：postscript（附言） pre- 表示“在前，在前面，提前”：prefix（前缀）, preface（前言）, preposition（介词）super- 表示“在…..之上，超级”：superstructure, supernatural, superpower, superman trans- 表示“移上，转上，在那一边”：translate, transform（转移）, transplant（移植）, transportation（交通） under- 表示“在…..下面，下的”：underline, underground, underwater, undershirt up- 表示“向上，向上面，在上”：upward(s), uphold, uphill（上坡） auto- 表示“自己，独立，自动”：automobile（自动车）, autobiography（自传） tele- 表示“远离”：television, telephone , telegram（电报）, telegraph（电报，抽象名词）, telescope（望远镜）
英语单词构词法(1)前缀 1.表示否定意义的前缀 1)纯否定前缀 a-, an-, asymmetry（不对称）anhydrous（无水的） dis- dishonest, dislike in-, ig-, il, im, ir, incapable, inability, ignoble, impossible, immoral, illegal, irregular ne-, n-, none, neither, never non-, nonsense neg-, neglect un- unable, unemployment 2)表示错误的意义 male-, mal-, malfunction, maladjustment(失调) mis-, mistake, mislead pseudo-, pseudonym(假名), pseudoscience 3)表示反动作的意思 de-, defend, demodulation(解调) dis-, disarm, disconnect un-, unload, uncover 4)表示相反，相互对立意思 anti-, ant- antiknock( 防震), antiforeign,(排外的) contra-, contre-, contro-, contradiction, controflow(逆流) counter-, counterreaction, counterbalance ob-, oc-, of-, op-, object, oppose, occupy with-, withdraw, withstand 2. 表示空间位置，方向关系的前缀 1)a- 表示“在……之上”，“向……”aboard, aside, 2)by- 表示“附近，邻近，边侧”bypath, bypass(弯路) 3)circum-, circu-, 表示“周围，环绕，回转”circumstance, circuit 4)de-, 表示“在下，向下”descend, degrade 5)en-, 表示“在内，进入”encage, enbed(上床) 6)ex-, ec-, es-, 表示“外部，外”exit, eclipse, expand, export 7)extra-, 表示“额外”extraction （提取） 8)fore- 表示“在前面”forehead, foreground 9)in-, il-, im-, ir-, 表示“向内，在内，背于”inland, invade, inside, import 10)inter-, intel-, 表示“在……间，相互”international, interaction, internet 11)intro-, 表示“向内，在内，内侧”introduce, introduce 12)medi-, med-, mid-, 表示“中，中间”Mediterranean, midposition 13)out-, 表示“在上面，在外部，在外” outline, outside, outward 14)over-, 表示“在上面，在外部，向上” overlook, overhead, overboard 15)post-, 表示"向后，在后边，次” postscript(附言)， 16)pre-, 表示"在前”在前面” prefix, preface, preposition 17)pro-, 表示“在前，向前” progress, proceed,