Computational Complexity in
Non-Turing Models of Computation
The What ,the Why and the How
Ed Blakey 1,2
Oxford University Computing Laboratory,Wolfson Building,Parks Road,Oxford,OX13QD,United Kingdom
Abstract
We preliminarily recap what is meant by complexity and non-Turing computation ,by way of explanation of our title,‘Computational Complexity in Non-Turing Models of Computation’.Based on investigation of a motivating example,we argue that traditional complexity theory does not adequately capture the true complexity of certain non-Turing computers,and,hence,that an extension of the theory is needed in order to accommodate such machines.We propose a framework of complexity that is not computation-model-dependent—that,rather,is extensible so as to accommodate diverse computational models—,and that allows meaningful comparison of computers’respective complexities,whether or not the comparison be with respect to di?erent resources ,and whether or not the computers be instances of di?erent models of computation .Whilst,we suggest,complexity theory is—without some modi?cation—of limited applicability to certain non-standard models,we hope that the ideas described here go some way to showing how such modi?cation can be made,and that members of the non-Turing-computation community—not least participants of Quantum Physics and Logic/Development of Computational Models 2008—?nd these ideas both useful and interesting.
Keywords:Computational complexity,non-standard/non-Turing computational models,precision.
This work forms part of the author’s ongoing studies for his doctoral degree.A more complete account of this project can be found in [3],which is available at https://www.doczj.com/doc/0e10498799.html,/~quee1871/transfer.pdf .
1What...
...do we mean by complexity and non-Turing computation ?
1We thank Bob Coecke and Jo¨e l Ouaknine (at Oxford)for their support,supervision and suggestions;Samson Abramsky and Peter Jeavons (at Oxford)for their useful comments about this project;Viv Kendon and colleagues (at Leeds)for useful discussions regarding the application of this work to quantum computing;Jonathan Mills (at Indiana,USA),Susan Stepney (at York)and others for their comments and suggestions made at Unconventional Computing 2007;and participants of the Second International Workshop on Natural Computing for their encouraging feedback and discussion.
2Email:
edward.blakey@https://www.doczj.com/doc/0e10498799.html,
Electronic Notes in Theoretical Computer Science 270 (2011) 17–28
1571-0661/$ – see front matter ? 2011 Elsevier B.V. All rights reserved.
https://www.doczj.com/doc/0e10498799.html,/locate/entcs
doi:10.1016/j.entcs.2011.01.003
In this section,we recall brie?y what is meant by computational complexity ,and note the concept’s (understandable)bias towards the Turing machine.We also note,however,the widespread discussion—and,indeed,practical use—of other models,and ask whether more is needed of complexity theory in order to allow suitable analysis of non-standard computers.
1.1Complexity
The ?eld of computational complexity strives to categorize problems according to the cost of their solution.This cost is the resource (run-time,memory space or similar)consumed during computation;complexity theorists are interested particularly in the increase in resource as the computation’s input grows.
So that the notions of complexity theory are well de?ned,the ?eld has largely been developed relative to a speci?c computation model :the Turing machine.This choice is rarely seen as problematic:a vast majority of practical computation con-forms to this model (speci?cally,real-world computations are typically performed by digital computers running programs that implement algorithms);the almost exclu-sive consideration of Turing machines is further bolstered—at least for those who overlook the distinction between computability and complexity—by the Church-Turing thesis 3.Consequently,resource is virtually always taken to be a property—usually run-time—of a Turing machine (or algorithm,random access machine or similar).4
Because of the prevalence of digital computers,then,and because of the con-jectured equivalence of the Turing machine to other computational models,this restriction of complexity theory to the exclusively algorithmic is seldom explicitly considered,let alone viewed as cause for concern.Indeed,the ?eld is very successful,not only theoretically,but also practically,o?ering excellent guidance,for example,on the allocation of computational e?ort.
1.2Non-Standard Computational Models
However,there has long been—and is today—an active community working on non-Turing forms of computer 5:
?mechanical means by which di?erential/integral equations are solved (e.g.,the Di?erential Analyzer;see [11]);
?the formation of soap bubbles between parallel plates,as used to ?nd minimal-length spanning networks (possibly with additional vertices)connecting given vertices (see [17]);
?
DNA-computing techniques that can tackle the directed Hamiltonian path prob-lem (see [2]),amongst other graph-theoretic/combinatorial problems;3
The Church-Turing thesis is introduced in [12]and discussed in [8]and [20],amongst many others.4Having said this,we acknowledge that consideration has been made—though in a largely ad hoc,and,hence,far from uni?ed,way—of complexity in non-standard computational models.We note,however,that the true complexity of such computation is not always captured by such consideration;notably,this is true in the (circuit-model)quantum-computer case—see Sect.3.1.
5We note also the growing recognition (see,e.g.,[1]and [14])of such computers’importance.
E.Blakey /Electronic Notes in Theoretical Computer Science 270(2011)17–28
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E.Blakey/Electronic Notes in Theoretical Computer Science270(2011)17–2819?standard(circuit-model[18])and non-standard(adiabatic[15,16],measurement-based[19],continuous-variable[9],etc.)quantum computers;
?optical methods o?ering novel approaches to the Travelling Salesman problem (see[13]),etc.;
?the work of participants of Quantum Physics and Logic/Development of Compu-tational Models2008;
?and so on.
Despite the dominance of digital computers,non-Turing systems are of increasing importance.
Just as we can analyse the complexity of algorithms(and Turing machines, etc.),so we wish to be able to analyse the complexity of non-standard computers, not least so that we can compare the e?ciency of our non-standard solutions with that of their digital counterparts.One may hope,then,that the complexity of non-Turing computers can be adequately analysed using the existing tools of traditional complexity theory.
We shall see that this is not the case:the Turing-machine-focused measures fail to capture the true complexity of certain systems.
2Why...
...do non-Turing computers warrant di?erent approaches to complexity analysis?
We claim above that traditional,Turing-based complexity theory overlooks the true complexity of some computing systems.In this section,we justify this claim by exhibiting just such a system and discussing its complexity.
2.1Analogue Factorization System
We now outline an analogue system that factorizes natural numbers.The descrip-tion given here is brief;for full details,see[4].(The interested reader should also see[7],the pending US patent,of which the system is the subject,applied for by IBM and with the author as sole inventor,and[6],which describes a modi?ed and in some senses improved version of the system.Finally,the system is used,as here, as a motivating example in[3].)
Just as with traditional algorithms,there is a practical limit to the size of num-bers that the system can factorize;in contrast with traditional algorithms,however, the system su?ers no increase in calculation time as the input number approaches this limit.Crucially for present purposes,the limit is not imposed by considerations (of run-time,memory space,etc.)made in traditional complexity theory,but rather by the increasing precision demanded of the user of the system.6
6We see here the?rst hints of a need for an extension of complexity theory.
Geometric formulation.
Note ?rst that the task of factorizing natural number n has a geometric formu-lation:the task is exactly that of ?nding points both in the integer grid Z 2and on the curve y =n x (the coordinates of such a point are two of the sought factors).Since factors of n are members of {0,...,n }(in fact,of {1,...,n }),we need search for grid/curve points (x,y )only in the region 0≤x,y ≤n ;and since (x,y )reveals the same factors as (y,x ),we need search only in the region 0≤x ≤y ≤n .The curve y =n x is a conic section (speci?cally,a hyperbola),and so is the intersection of the (x,y )-plane and a cone ;the factorization method’s implementation exploits this fact.
Implementation of the grid.
We implement the part of the integer grid that is of interest (i.e.,that lies in
the region identi?ed above)using a source S (of wavelength 2n )of transverse waves,re?ected by mirrors M 1,M 2and M 3so as to produce a certain interference pattern;
the points of maximal wave activity in this pattern model integer grid points,with maximally active point a n ,b n ,0 modelling grid point (a,b )(where a,b ∈Z ).7Since the wavelength of radiation from S depends on n ,its being set forms part of the computation’s input process.
See Fig.1for the apparatus used in implementing the integer grid (in which B is a black body that absorbs some radiation from S ),Fig.2for the route of propagation of a sample ray 8,and Fig.3for the maxima (shown as dots)of the resultant interference pattern within the region R :={(x,y,0)|0≤x ≤y ≤1}that,since a n ,b n ,0
models (a,b ),models the region 0≤x ≤y ≤n of interest (this ?gure takes as its example n =5).
Implementation of the cone.
The cone is implemented by a source P n of radiation (the vertex of the cone)and a part-circular sensor C n (the circle is a cross section of the cone).9See Fig.4.The subscripts re?ect the fact that the positions of P n and C n depend on n ;the positioning of these components forms part of the input process for the computation.Interpreting output.
Having described the apparatus and alluded to the input method (namely,set-ting to the appropriate,n -dependent values the wavelength of S and positions of P n and C n ),we turn to the interpretation of output.Radiation arriving from P n at a point on C n displays high-amplitude interference (due to the pattern of waves 7In fact,these maximally active points model those integer-grid points that have coordinates of the same parity .This is su?cient provided that we assume n to be odd,for then any factors of n are odd.(Should a factorization be required of an even number,it is computationally trivial—in the Turing-machine realm—iteratively to divide by two until an odd number,which can be factorized as described here,is obtained.)8Note that the ray is re?ected back along itself by M 3;this produces a standing wave .9It is proven in [4]that the curve of C n is the circular arc produced by projecting from P n the curve G n := (x,y,0)∈R |1xy =n onto the plane y =2?x .Hence,radiation arriving from P n at a point on
C n passes through the plane z =0at a point (x,y,0)such that 1xy =n .
E.Blakey /Electronic Notes in Theoretical Computer Science 270(2011)17–28
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E.Blakey/Electronic Notes in Theoretical Computer Science270(2011)17–2821
grid.
Fig.1.The layout of the apparatus that implements the integer Array
.
Fig.2.The path of a ray propagating from S via the three mirrors M i back to S Array Fig.3.The maximum amplitude points in the region R,where n=5.
Fig.4.The layout of the apparatus that implements the cone.
from S )if and only if the point (x,y,0)at which it meets the (x,y )-plane models an
integer point;that is,if and only if 1x ,1y ∈Z .Further,by construction of P n and C n ,1x ·1y =n (see footnote 9).Hence,this radiation displays high-amplitude interference
if and only if 1x and 1y are factors of n .Thus,to interpret the results,we measure the coordinates of a point (that which displays high-amplitude interference)on C n and convert them into those of a point (that through which the ray passes)in the
(x,y )-plane.Explicitly,radiation from P n incident on a point (a,2?a,c )on C n passes through a n (2?a ) 2?a na ,0 ;hence,if the radiation arriving from P n at (a,2?a,c )on C n displays high-amplitude interference,then a n (2?a ), 2?a na ,0 models an integer point on the hyperbola under consideration: n (2?a )a and na 2?a are factors of n ;conversely,all factors have such a point on C n .Having set up the apparatus as described here,then,the factors of n are so found.
We have outlined an analogue factorization system.10It o?ers much-improved run-times when compared with existing,algorithmic solutions,largely because it di-rectly,physically implements the problem rather than converting the problem into a contrived instance of the standard computation model.The polynomial time and space complexities 11serve,however,to highlight not the power of the system but the incompleteness of traditional complexity theory.As n increases,the system does require exponentially more resource (though neither speci?cally time nor space);in particular,the precision with which n must be input (by setting the wavelength of S and the positions of P n and C n )and its factors read (by measuring the positions of 10We reiterate that [4]o?ers a more complete description of the system;it also o?ers proof of the system’s correct functioning.
11These are discussed further in the following section,and formalized in [3].
E.Blakey /Electronic Notes in Theoretical Computer Science 270(2011)17–28
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points on C n )increases exponentially with n .This suggests that,for some comput-ers,traditional,‘algorithmic’complexity theory is inadequate;we aim to introduce notions of complexity more suitable in such contexts.
2.2Complexity of the System
Using the system.
The use of the system to factorize n consists of:
(i)calculation of the values 2n (to be used as the wavelength of S )and 2n (to be used as the z -coordinate of P n and of the centre of the circle of C n );(ii)supply of n to the system,by adjusting the wavelength of S and the height
(z -coordinate)of P n and C n in accordance with the values found during (i );(iii)interference of the radiation in the system,which entails propagation of the ra-
diation over a ?xed distance (since the same apparatus is—bar the adjustments made in (ii )—used for all values of n );
(iv)measurement of the positions of high-amplitude interference points on the sen-
sor C n ;and
(v)conversion of the positions measured during (iv )into factors:(a,2?a,c )→ n (2?a )a , na 2?a .
We consider now the computational complexity,with respect to several resources (formally de?ned in [3]),of these steps of the factorization system’s use.
Time complexity.Consider ?rst time ;the time complexity of the system,we claim,is polynomial in the size (i.e.,number of digits)of the input value.Note that,in the Turing-machine realm,steps (i )and (v )above take only poly-nomially long (in the size of n ),the former since 2n and 2n need be calculated
only with su?cient precision that n can be retrieved given that n ∈Z ,and the latter since sought values n (2?a )a and na 2?a are integers.Note further that steps (ii )–(iv )take a constant time:larger values of n take no longer to process in accor-dance with these stages.Notably,step (iii ),during which the actual factorization is performed,takes constant time;compare this with known algorithmic methods,where computation time increases exponentially with the size of n (see,e.g.,[10]).Thus,the time complexity of the system as a whole is,as claimed,polynomial in the size of the input.
E.Blakey /Electronic Notes in Theoretical Computer Science 270(2011)17–2823
Space complexity.
Similarly,when considering the resource of space ,12we see that only the Turing-machine calculations of steps (i )and (v )(which essentially prepare input and in-terpret output)consume an increasing volume as n increases,and these only a polynomially increasing volume (in the size of the input);and for steps (ii )–(iv ),the same,?xed-size apparatus—occupying the same,?xed space—is used for all values of n (though the positions of P n and C n depend on n ,there exists a ?nite,n -independent,bounding cuboid in which the apparatus lies for all n ).Thus,the space complexity of the system is polynomial in the size of the input.
The resources of time and space are arguably of paramount relevance when con-sidering instances (Turing machines,random access machines,etc.)of the standard,algorithmic computational model.Notions of complexity developed with only these instances in mind,however,are understandably poor at capturing the complexity of instances of wildly di?erent models;the factorization system above does indeed have polynomial time and space complexities,and yet does require exponentially increasing resource as n increases.Notably,larger values of n require exponentially increasingly precise manipulation of the input parameters (the wavelength of S and the positions of P n and C n )and exponentially increasingly precise measurement of the output parameters (the coordinates of points on C n ),and there is no reason for which we should not view required precision as a resource.Accordingly,we consider now the precision complexity of the system,which is certainly not polynomial,and hence better captures the system’s true complexity than do the resources of time and space.
Precision complexity.
The intention of precision complexity is to capture the lack of robustness against input/output imprecision of a ‘physical’(e.g.,analogue/optical/chemical)computer.We consider the example of setting the wavelength in our factorization system;other input parameters (the positions of P n and C n )and output parameters (the positions of points on C n ),which we omit for brevity,13can be analysed similarly.(A formal account/de?nition of precision complexity is given in [3].)
Given n ,which we wish to factorize,we intend to set the wavelength to 2n .
In practice,due to technological limitations on our control over the wavelength (we have in mind imprecise use of a variable resistor or similar),we may set the wavelength to 2n ,which we know only to lie in 2n ? ,2n + for some real error term .However,we may engineer the system (using standard analogue techniques)so that non-integer input values are rounded o?so as to correct ‘small’errors:given wavelength 2x (for arbitrary x ∈R ),the value to be factorized is taken to be x +12 .So,provided that the supplied wavelength 2n ∈ 2n ? ,2n + falls in the interval 12Space,as traditionally encountered with Truing machines,etc.,can be viewed as the storage capacity of the memory required by a computation;we consider the analogous notion of required physical volume .13The omission,whilst partly for brevity,also re?ects the redundancy of consideration of other parame-ters’precision requirements:once the setting of the wavelength has been shown to require exponentially increasing precision,then we have that the overall precision complexity is exponential,regardless of the contribution from other parameters.
E.Blakey /Electronic Notes in Theoretical Computer Science 270(2011)17–28
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2 n+12,2
n?12
of values‘corrected’to n—i.e.,provided that <1
n(n+12)
—,then the
system processes the correct input value.Note that this constraint on implies that the precision required of us when setting the wavelength increases quadratically with n,and hence exponentially with the size of n.
We see,then,that the system’s precision complexity,which we informally in-troduce here and,we iterate,treat formally in[3],is exponential,and therefore of much greater signi?cance than the system’s polynomial time/space complexity.The signi?cant complexity measure is overlooked by traditional complexity theory.
3How...
...should we measure non-Turing computers’complexity,and how can such complexity be meaningfully compared with that of Turing machines?
There are two aspects to the way in which,we suggest,complexity in non-standard computation should be analysed.
?First,we advocate consideration of more diverse selections of resources;for exam-ple,we see above that not only traditional,algorithmic measures such as time and space,but also more physical measures such as precision,should be considered. See Sect.3.1.
?Secondly,we should like to be able to compare in a meaningful way the respec-tive complexities of instances of di?erent computational models,with respect to di?erent complexity resources;we accordingly advocate use of the notion of dom-inance,which o?ers a criterion that tells us which of a computation’s resources are‘relevant’;once such are identi?ed,they can be compared within the usual ‘∈O’pre-ordering.See Sect.3.2.
3.1Resources in Non-Turing Computation
We note above the following:
?that traditional complexity theory and the resources(time,space,etc.)considered therein are inspired by the Turing-machine model of computation,almost totally to the exclusion of other models;
?that,while this is clearly adequate when considering Turing machines,certain non-standard computers(quantum computers being a timely example)are inad-equately catered for by these resources;and
?that it is possible(and even natural,once the problem has been acknowledged) to de?ne resources(e.g.,precision)that better capture the true complexity of non-Turing computers.
The solution is obvious,then:when working with a non-standard compu-tational model,we should consider which resources—both algorithmic and non-algorithmic—are consumed during computation,and should explicitly measure the complexity of our computation with respect to these resources;this gives a more
E.Blakey/Electronic Notes in Theoretical Computer Science270(2011)17–2825
complete picture,and more con?dence in our understanding,of the computation’s complexity.
The need for consideration of di?erent resources is particularly evident in the quantum-computer case.An arbitrary algorithm (or Turing machine or similar)can,by de?nition of complete,be expressed as a conversion of input to output via operations exclusively taken from some complete set of what are deemed to be ‘atomic’operations.For a given input value,the number of such operations performed during this conversion is an accurate measure (or,depending on view-point,a de?nition)of run-time.Similarly,an arbitrary quantum computation can be expressed as the preparation of several quantum bits,followed by a sequence of applications to subsets of these quantum bits of ‘atomic’unitary operations taken from a complete set,followed by a measurement of the system.As in the classical case,an enumeration of the invocations of these atomic operations gives a mea-sure of the system’s complexity;indeed,this is the basis of an existing de?nition of complexity of circuit-model quantum computing devices (see [18]).Also as in the classical case,however,the result is essentially a measure of run-time,which is not,we suggest,particularly relevant to quantum systems.14
By introducing and considering new resources,speci?cally ones similar to pre-cision,we may,we suggest,better encapsulate the true complexity of a quantum system;this complexity arises,after all,because of our limited ability to take precise measurements from the system.
3.2Comparing Complexity
Whereas reasoning directly about the computational complexity of a problem seems inherently di?cult,it is relatively easy (once appropriate resources are considered)to ascertain the complexity of speci?c methods (algorithms,analogue computers,Turing machines,etc.)that solve the problem .Consequently,our sole understand-ing of a problem’s complexity is,in the majority of cases,gleaned via our having determined the complexity of solution methods for the problem;speci?cally,we have that the problem’s complexity is bounded from above by the complexity of the most e?cient known solution method.
In order to improve such bounds,it is desired to consider as large a set as is pos-sible of solution methods for a problem.Each set so considered in practice,however,is likely to be of solution methods taken from a single model of computation (often that of the Turing machine).This is a necessary evil of our inability meaningfully to compare the complexity of instances of di?erent computation models.
Required,then,is a more general framework in which to study complexity;in particular,we wish to be able to use the framework to consider on a consistent and comparable footing the complexity—with respect to several notions of resource—of instances of diverse models of computation;only when we can meaningfully 14The advantage of quantum computers over their classical counterparts arises primarily from the use of entangled states and the e?ective parallelism that such use allows;a disadvantage is the strictly constrained way in which information can be read from the quantum system.The run-time of such a system,then,is a re?ection of neither the ‘amount of computation’being performed (due to the parallelism)nor the ‘di?culty’in using the system (which chie?y arises during measurement).
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E.Blakey/Electronic Notes in Theoretical Computer Science270(2011)17–2827 compare,say,the respective complexities of a Turing machine and a DNA computer can we begin to consider larger,model-heterogeneous sets of solution methods,and hence obtain improved bounds on the complexity of problems.Accordingly,we introduce the notion of dominance of resource.
3.2.1Dominance.
Recall that,in the complexity analysis of the factorization system above,we suggest that precision,on which the system has an exponential dependency,is more relevant than either time or space,on each of which it has polynomial dependency.This can be formalized(and a general concept abstracted)by noting that T,S∈O(P), but neither P∈O(T)nor P∈O(S),where P,T and S stand respectively for the precision,time and space complexity functions;we say that,relative to{P,T,S}, precision is dominant.More generally,relative to a set{X1,...,X k}of complexity functions(with respect to respective resources x1,...,x k),resource x i is dominant
if and only if X i∈O(X j)?X j∈O(X i)for all j.
By considering for a given solution method(e.g.,Turing machine/analogue com-puter/quantum system)only the dominant resources,we focus on the relevant measures for the method—dominance formalizes a resource’s relevance:resources that are dominant impose the asymptotically greatest cost,to the extent that non-dominant resources may be disregarded as irrelevant.Further,we can compare solu-tion methods according to their relevant(i.e.,dominant)resources(using the‘∈O’pre-ordering).We therefore have a framework in which can be made meaningful and consistent comparisons of computation-model-heterogeneous sets of computers; the framework can accommodate instances of various models of computation,and provide structure according to cost in terms of various resources.
(This framework and the notion of dominance on which it is based are investi-gated in greater detail in[5].)
4Conclusion
Complexity theory has developed with a bias towards the Turing-machine model. This is readily explained,and poses no problem in‘standard’use;we note,how-ever,that the?eld is—without some modi?cation—of limited applicability to non-standard models.We hope that the ideas described here(which are explored more fully in[3])go some way to showing how such modi?cation can be made,and that members of the QPL/DCM2008community?nd these ideas both useful and in-teresting.
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[20]Sipser,M.,“Introduction to the Theory of Computation”,PWS (1997)E.Blakey /Electronic Notes in Theoretical Computer Science 270(2011)17–28
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in，on，at的时间用法和地点用法 一、in, on, at的时间用法 ①固定短语： in the morning/afternoon/evening在早晨/下午/傍晚, at noon/night在中午/夜晚, （不强调范围，强调的话用during the night） early in the morning=in the early morning在大清早， late at night在深夜 on the weekend在周末（英式用at the weekend在周末，at weekends每逢周末） on weekdays/weekends在工作日/周末， on school days/nights在上学日/上学的当天晚上， ②不加介词 this, that, last, next, every, one, yesterday, today, tomorrow, tonight，all，most等之前一般不加介词。如， this morning 今天早晨 （on）that day在那天（that day更常用些） last week上周 next year明年 the next month第二个月（以过去为起点的第二个月，next month以现在为起点的下个月） every day每天 one morning一天早晨 yesterday afternoon昨天下午 tomorrow morning明天早晨 all day/morning/night整天/整个早晨/整晚（等于the whole day/morning/night） most of the time （在）大多数时间 ③一般规则 除了前两点特殊用法之外，其他≤一天，用on，＞一天用in，在具体时刻或在某时用at（不强调时间范围） 关于on 生日、on my ninth birthday在我九岁生日那天 节日、on Teachers’Day在教师节 （注意：节日里有表人的词汇先复数再加s’所有格，如on Children’s Day, on Women’s Day, on Teachers Day有四个节日强调单数之意思，on Mother’s Day, on Father’s Day, on April Fool’s Day, on Valenti Day） 星期、on Sunday在周日，on Sunday morning在周日早晨 on the last Friday of each month 在每个月的最后一个星期五 日期、on June 2nd在六月二日 on the second (of June 2nd) 在六月的第二天即在六月二日 on the morning of June 2nd在六月二日的早晨，on a rainy morning在一个多雨的早晨 on a certain day 在某天 on the second day在第二天（以过去某天为参照） 注意：on Sunday在周日，on Sundays每逢周日（用复数表每逢之意），every Sunday每个周日，基本一个意思。 on a school day 在某个上学日，on school days每逢上学日。on the weekend在周末，on weekends每逢 周末。 关于in in June在六月 in June, 2010在2010年六月
常用标点符号用法简表 标点符号栏目对每一种汉语标点符号都有详细分析，下表中未完全添加链接，请需要的同学或朋友到该栏目查询。名称符号用法说明举例句号。表示一句话完了之后的停顿。中国共产党是全中国人民的领导核心。逗号，表示一句话中间的停顿。全世界各国人民的正义斗争，都是互相支持的。顿号、表示句中并列的词或词组之间的停顿。能源是发展农业、工业、国防、科学技术和提高人民生活的重要物质基础。分号；表示一句话中并列分句之间的停顿。不批判唯心论，就不能发展唯物论；不批判形而上学，就不能发展唯物辩证法。冒号：用以提示下文。马克思主义哲学告诉我们：正确的认识来源于社会实践。问号？用在问句之后。是谁创造了人类？是我们劳动群众。感情号①！1.表示强烈的感情。2.表示感叹句末尾的停顿。战无不胜的马克思主义、列宁主义、毛泽东思想万岁！引号 ②“ ” ‘ ’ ╗╚ ┐└1.表示引用的部分。毛泽东同志在《论十大关系》一文中说：“我们要调动一切直接的和间接的力量，为把我国建设成为一个强大的社会主义国家而奋斗。”2.表示特定的称谓或需要着重指出的部分。他们当中许多人是身体好、学习好、工作好的“三好”学生。 3.表示讽刺或否定的意思。这伙政治骗子恬不知耻地自封为“理论家”。括号③（）表示文中注释的部分。这篇小说环境描写十分出色，它的描写（无论是野外，或是室内）处处与故事的发展扣得很紧。省略号④……表示文中省略的部分。这个县办工厂现在可以生产车床、电机、变压器、水泵、电线……上百种产品。破折号⑤——1.表示底下是解释、说明的部
分，有括号的作用。知识的问题是一个科学问题，来不得半点的虚伪和骄 傲，决定地需要的倒是其反面——诚实和谦逊的态度。2.表示意思的递进。 团结——批评和自我批评——团结3.表示意思的转折。很白很亮的一堆洋 钱！而且是他的——现在不见了！连接号⑥—1.表示时间、地点、数目等 的起止。抗日战争时期（1937-1945年）“北京—上海”直达快车2.表 示相关的人或事物的联系。亚洲—太平洋地区书名号⑦《》〈〉表示 书籍、文件、报刊、文章等的名称。《矛盾论》《中华人民共和国宪法》《人 民日报》《红旗》杂志《学习〈为人民服务〉》间隔号·1.表示月份和日期 之间的分界。一二·九运动2.表示某些民族人名中的音界。诺尔曼·白求 恩着重号.表示文中需要强调的部分。学习马克思列宁主义，要按照毛泽 东同志倡导的方法，理论联系实际。······
In\ on\ at (time) at 用在具体某一时刻eg at 11:00 at 4:30 在节假日的全部日子里at Christmas 习惯用法at noon at weekends\ at the weekend at night at breakfast\lunch\supper on 具体到某一天；某一天的早晨，中午或晚上on May the first on Sunday morning 对具体某一天的早晨，中午，晚上进行详细的描述on a sunny morning on a windy night 节日的当天；星期on Women?s Day on Monday In 用在年；月；季节in spring in 2012 in August 后面+一段时间表示将来时in two days 习惯用法in the morning\in the afternoon\in the evening “\”以this, that, last, next, some, every, one, any,all开始的时间副词之前的at\on\in 省略在today, tomorrow, yesterday, the day after tomorrow, tomorrow morning，yesterday afternoon，the day before yesterday 之前的介词必须省略 Practice ___ summer ____ 2012 ____ supper ___ 4:00 ___ June the first ___yesterday morning ____ New Year?s Day ___ Women?s Day ___ the morning ____ the morning of July the first ____ 2014 ___ tomorrow morning ____ midnight 1.—What are you doing ____ Sunday? And what is your wife doing ___ the weekend? 2. He?ll see you ____ Monday. And he…ll see your brother ____next Monday. 3. They often go out ___ the evenings. But they don?t go out ____ Sunday evenings. 4. Do you work ____ Fridays? Does she work _____ every Friday? 5. They usually have a long holiday ___ summer. But their son can only have a short holiday ___ Christmas. 6. Paul got married ___ 2010, He got married ___ 9 o?clock ___ 19 May 2010. His brother got married ___ May, 2011. His sister is getting married ___ this year. 1.—When will Mr Black come to Beijing? ---_______ September 5 A. on B. to C. at D. in 2. The twins were born ____ a Friday evening. A. on B. of C. at D. in 3. It?s the best time to plant ____ spring. A. on B. in C. at D.\ 4. ____ the age of twelve, Edison began selling newspaper on train. A. On B. At C. In D.By 5. She has been an English teacher ____ 2000. A. for B. since C. in D.on 6.I have studied English _____ 2003. A. since B. for C. from D.in
一、基本定义 句子，前后都有停顿，并带有一定的句调，表示相对完整的意义。句子前后或中间的停顿，在口头语言中，表现出来就是时间间隔，在书面语言中，就用标点符号来表示。一般来说，汉语中的句子分以下几种： 陈述句： 用来说明事实的句子。 祈使句： 用来要求听话人做某件事情的句子。 疑问句： 用来提出问题的句子。 感叹句： 用来抒发某种强烈感情的句子。 复句、分句： 意思上有密切联系的小句子组织在一起构成一个大句子。这样的大句子叫复句，复句中的每个小句子叫分句。 构成句子的语言单位是词语，即词和短语（词组）。词即最小的能独立运用的语言单位。短语，即由两个或两个以上的词按一定的语法规则组成的表达一定意义的语言单位，也叫词组。 标点符号是书面语言的有机组成部分，是书面语言不可缺少的辅助工具。它帮助人们确切地表达思想感情和理解书面语言。 二、用法简表 名称
句号① 问号符号用法说明。？1．用于陈述句的末尾。 2．用于语气舒缓的祈使句末尾。 1．用于疑问句的末尾。 2．用于反问句的末尾。 1．用于感叹句的末尾。 叹号！ 2．用于语气强烈的祈使句末尾。 3．用于语气强烈的反问句末尾。举例 xx是xx的首都。 请您稍等一下。 他叫什么名字？ 难道你不了解我吗？为祖国的繁荣昌盛而奋斗！停止射击！ 我哪里比得上他呀！ 1．句子内部主语与谓语之间如需停顿，用逗号。我们看得见的星星，绝大多数是恒星。 2．句子内部动词与宾语之间如需停顿，用逗号。应该看到，科学需要一个人贡献出毕生的精力。 3．句子内部状语后边如需停顿，用逗号。对于这个城市，他并不陌生。 4．复句内各分句之间的停顿，除了有时要用分号据说苏州园林有一百多处，我到过的不外，都要用逗号。过十多处。 顿号、用于句子内部并列词语之间的停顿。
介词in on at 表示时间的用法及区别 Step1 Teaching Aims 教学生掌握时间介词in,on和at的区别及用法。 Step2 Teaching Key and Difficult Points 教学生掌握时间介词in,on和at的区别及用法。 Step3 Teaching Procedures 1.用in的场合后所接的都是较长时间 (1)表示“在某世纪/某年代/特定世纪某年代/年/季节/月”这个含义时，须用介词in Eg: This machine was invented in the eighteenth century. 这台机器是在18世纪发明的。 、 She came to this city in 1980. 他于1980年来到这个城市。 It often rains here in summer. 夏天这里常常下雨。 (2)表示“从现在起一段时间以后”时，须用介词in。(in+段时间表将来) Eg: They will go to see you in a week. 他们将在一周后去看望你。
I will be back in a month. 我将在一个月后回来。 (3)泛指一般意义的上、下午、晚上用in, in the morning / evening / afternoon Eg: They sometimes play games in the afternoon. 他们有时在下午做游戏。 Don't watch TV too much in the evening. 晚上看电视不要太多。（4）A. 当morning, evening, afternoon被of短语修饰，习惯上应用on, 而不用in. Eg: on the afternoon of August 1st & B. 但若前面的修饰词是early, late时，虽有of短语修饰，习惯上应用in, 而不用on. Eg: in the early morning of September 10th 在9月10的清晨； Early in the morning of National Day, I got up to catch the first bus to the zoo. 国庆节一清早，我便起床去赶到动物园的第一班公共汽车。 2.用on的场合后所接的时间多与日期有关 （1）表示“在具体的某一天”或（在具体的某一天的）早上、中午、晚上”，或“在某一天或某一天的上午，下午，晚上”等，须用介
介词in, on, at在表示时间时的用法区别 ①in时间范围大（一天以上）如：in Tanuary, in winter, in 1999;泛指在上午，下午，晚上，如：in the morning(afternoon, evening). 习惯用法：in the daytime 在白天。 ②on指在某一天或某一天的上午，下午，晚上，如：on Monday, on Sunday afternoon, on July 1, 1999 ③at时间最短，一般表示点时间，如at six o’clock, at three thirty.习惯用法：at night, at noon, at this time of year. in, on和at在表达时间方面的区别 in 表示在某年、某季节、某月、某周、某天和某段时间 in a year在一年中 in spring 在春季 in September 在九月 in a week 在一周中 in the morning/afternoon/evening 在上午/下午/傍晚 但在中午，在夜晚则用at noon/night on 表示某一天或某一天的某段时间 on Monday 在周一 on Monday afternoon 在周一下午 on March 7th 在3月7日 on March 7th, 1998. 在1998年3月7日 on the morning of March 7th, 1998. 在1998年3月7日上午
at 表示某个具体时刻。 at eight o’clock 在8点钟 at this time of the year 在一年中的这个时候 at the moment 在那一时刻 at that time 在那时 注意：在英语中，如果时间名词前用this, last, next 等修饰时，像这样的表示，“在某时”的时间短语前，并不需要任何介词。 例如：last month, last week, this year, this week, next year, the next day, the next year 等。 1．What’s the weather like in spring/summer/autumn/winter in your country? 你们国家春天/夏天/秋天/冬天的天气怎么样？ in 在年、月、周较长时间内 in a week 在里面 in the room 用某种语言 in English 穿着 in red on 某日、某日的上下午on Sunday afternoon 在……上面 on the desk 靠吃……为生live on rice 关于 a book on Physics 〔误〕We got to the top of the mountain in daybreak. 〔正〕We got to the top of the mountain at day break. 〔析〕at用于具体时刻之前，如：sunrise, midday, noon, sunset, midnight, night。〔误〕Don't sleep at daytime 〔正〕Don't sleep in daytime. 〔析〕in 要用于较长的一段时间之内，如：in the morning / afternoon, 或in the week / month / year. 或in spring / supper /autumn / winter等等。 〔误〕We visited the old man in Sunday afternoon. 〔正〕We visited the old man on Sunday afternoon. 〔析〕in the morning, in the afternoon 如果在这两个短语中加入任何修饰词其前面的介
精心整理in，on，at的时间用法和地点用法 一、in,on,at的时间用法 1、固定短语： inthemorning/afternoon/evening在早晨/下午/傍晚, 2 （on thenextmonth第二个月（以过去为起点的第二个月，nextmonth以现在为起点的下个月） everyday每天 onemorning一天早晨 yesterdayafternoon昨天下午
tomorrowmorning明天早晨 allday/morning/night整天/整个早晨/整晚（等于thewholeday/morning/night）mostofthetime（在）大多数时间 3、一般规则 除了前两点特殊用法之外，其他≤一天，用on，＞一天用in，在具体时刻或在某时用at（不强调时间范围） 关于 On 1 2) 3) （注意：节日里有表人的词汇先复数再加s’所有格，如 onChildren’sDay,onWomen’sDay,onTeachers’Day有四个节日强调单数之意思，onMother’sDay,onFather’sDay,onAprilFool’sDay,onValentine’sDay） 星期、onSunday在周日，onSundaymorning在周日早晨onthelastFridayofeachmonth在每个月的最后一个星期五
日期、onJune2nd在六月二日 onthesecond(ofJune2nd)在六月的第二天即在六月二日onthemorningofJune2nd在六月二日的早晨，onarainymorning在一个多雨的早晨 onacertainday在某天 onthesecondday在第二天（以过去某天为参照） 关于 In 1 2) InJune在六月 inJune,2010在2010年六月 in2010在2010年 inamonth/year在一个月/年里（在将来时里翻译成一个月/年之后） inspring在春天
常用标点符号主要用法 问号 1、用在特指问句后。如：(7)你今年多大了？ 2、用在反问句后。如：(8)为什么我们不能刻苦一点呢？ ?提示：反问句若语气缓和，末尾可用句号；若语气重可用感叹号。如：(9)国家 主席可以活活被整死；堂堂大元帅受辱骂；……这哪里还有什么尊重可言！ 3、用在设问句后。如：(10)我们能让你计划实现吗？不会的。 4、用在选择问句中。如：(11)我们是革命呢，还是要现大洋？ ( 12)你到底是去，还是不去？ 5、用在表疑问的独词句后。如：(13)我？不可能吧。 ?提示：若疑问句为倒装句，问号应放在句末。如：(14)到底出了什么问题，你的 车？(若说成：“到底出了什么问题？你的车。”则错误。) ?特别提示： 句号、问号均表示句末停顿。句号用于陈述句末尾，问号用于疑问句末尾。有些句 中虽有疑问词，但全句并不是疑问句，句末只能用句号，不能用问号。 例如：(17)……最后应求出铜块的体积是多少？ (18)面对千姿百态、纷繁芜杂的期刊世界，有哪位期刊编辑不想通过期刊版面设 计为刊物分朱布白、添花增色呢？ (19)关于什么是智力？国内外争论多年也没有定论。 (17) (18) ( 19)三句都是非疑问句，(17) (18)句中问号均应改为句号，(19)句中的问号应改为逗号。 感叹号 ?特别提示： 1、在表感叹或祈使语气的主谓倒装句中，感叹号要放在句末。 如：(20)多么雄伟壮观啊，万里长城！ 2、句前有叹词，后是感叹句，叹号放在句末。 如：(21)啊，这儿多么美丽！ 下面介绍句中点号的用法。句中点号包括逗号、分号、顿号、和冒号四种。 逗号 提示：复句内各分句之间的停顿，除了有时用分号外，都要用逗号。 顿号 用于句中并列的词、词组之间较小的停顿。 如：(22)邓颖超的品德、人格、风范为中华民族树立了一座精神丰碑。 (23)从1918年起，鲁迅陆续发表了《狂人日记》、《药》、《祝福》等短篇小说。 ?特别提示：以下九种情况不用顿号。 1、不定数的两个数字间不用顿号。 如：(24)你的年龄大概是十六七岁。(不能写成“十六、七岁”) ?【注意】相邻的两个数字而非约数之间要用顿号。
i n o n a t的时间用法和 地点用法版 集团档案编码：[YTTR-YTPT28-YTNTL98-UYTYNN08]
i n，o n，a t的时间用法和地点用法 一、in,on,at的时间用法 1、固定短语： inthemorning/afternoon/evening在早晨/下午/傍晚, atnoon/night在中午/夜晚,（不强调范围，强调的话用duringthenight）earlyinthemorning=intheearlymorning在大清早， lateatnight在深夜 ontheweekend在周末（英式用attheweekend在周末，atweekends每逢周末）onweekdays/weekends在工作日/周末， onschooldays/nights在上学日/上学的当天晚上， 2、不加介词 this,that,last,next,every,one,yesterday,today,tomorrow,tonight，all，most等之前一般不加介词。如， thismorning今天早晨 （on）thatday在那天（thatday更常用些） lastweek上周 nextyear明年 thenextmonth第二个月（以过去为起点的第二个月，nextmonth以现在为起点的下个月） everyday每天 onemorning一天早晨 yesterdayafternoon昨天下午 tomorrowmorning明天早晨
allday/morning/night整天/整个早晨/整晚（等于 thewholeday/morning/night） mostofthetime（在）大多数时间 3、一般规则 除了前两点特殊用法之外，其他≤一天，用on，＞一天用in，在具体时刻或在某时用at（不强调时间范围） 关于on On指时间表示： 1）具体的时日和一个特定的时间，如某日，某节日，星期几等。Hewillcometomeetusonourarrival. OnMay4th(OnSunday,OnNewYear’sday,OnChristmasDay),therewillbeacelebra tion. 2)在某个特定的早晨，下午或晚上。 Hearrivedat10o’clocko nthenightofthe5th. Hediedontheeveofvictory. 3)准时，按时。 Iftherainshouldbeontime,Ishouldreachhomebeforedark. 生日、onmyninthbirthday在我九岁生日那天 节日、onTeachers’Day在教师节 （注意：节日里有表人的词汇先复数再加s’所有格，如 onChildren’sDay,onWomen’sDay,onTeachers’Day有四个节日强调单数之意思， onMother’sDay,onFather’sDay,onAprilFool’sDay,onValentine’sDay）星期、onSunday在周日，onSundaymorning在周日早晨
介词in,on与at表时间的用法 at < 天(eg. noon, dawn, night, one’ clock) on = 天(Monday, 30th June, New Year’s Day, Mother’s Day) in > 天(2008, summer, April, 还有早午晚) 用in的场合后所接的都是较长时间 (1)表示“在某世纪/某年代/特定世纪某年代/年/季节/月”这个含义时,须用介词in Eg: This machine was invented in the eighteenth century. 这台机器是在18世纪发明的。 This incident happened in the 1970s. 该事件发生在20世纪70年代。 She came to this city in 1980. 他于1980年来到这个城市。 It often rains here in summer. 夏天这里常常下雨。 (2)表示“从现在起一段时间以后”时,须用介词in。(in+段时间表将来) Eg: They will go to see you in a week. 他们将在一周后去看望你。 I will be back in a month. 我将在一个月后回来。 (3)泛指一般意义的上、下午、晚上用in, in the morning / evening / afternoon Eg: They sometimes play games in the afternoon. 他们有时在下午做游戏。
Don't watch TV too much in the evening. 晚上看电视不要太多。 (4)A. 当morning, evening, afternoon被of短语修饰,习惯上应用on, 而不用in. Eg: on the afternoon of August 1st (5)B. 但若前面的修饰词是early, late时,虽有of短语修饰,习惯上应用in, 而不用on. Eg: in the early morning of September 10th 在9月10的清晨; in the late afternoon of September 12th 在9月12日的傍晚。 Early in the morning of National Day, I got up to catch the first bus to the zoo. 国庆节一清早,我便起床去赶到动物园的第一班公共汽车。 用on的场合后所接的时间多与日期有关 (1)表示“在具体的某一天”或(在具体的某一天的)早上、中午、晚上”,或“在某一天或 某一天的上午,下午,晚上”等,须用介词on。 Eg: Jack was born on May 10th, 1982. 杰克生于1982年5月10日。 They left on a rainy morning. 他们是在一个雨天的早上离开的。 He went back to America on a summer afternoon. 他于一个夏天的下午返回了美国。
公文写作中标点符号的使用规范 《中华人民共和国国家标准》（GB|T15834—1995）中有《标点符号用法》，其中对标点符号的使用作了明确的规定： 标点符号是辅助文字记录语言的符号，是书面语的有机组成部分，用来表示停顿、语气以及词语的性质和作用。 常用的标点符号有16种，分点号和标号两大类。 点号的作用在于点断，主要表示说话时的停顿和语气。点号又分为句末点号和句内点号——句末点号用在句末，有句号、问号、叹号3种，表示句末的停顿，同时表示句子的语气；句内点号用在句内，有逗号、顿号、分号、冒号4种，表示句内的各种不同性质的停顿。 标号的作用在于标明，主要标明语句的性质和作用。常用的标号有9种，即：引号、括号、破折号、省略号、着重号、连接号、间隔号、书名号和专名号。 一、常用标点符号用法简表 名称符号用法说明举例 句号。表示一句话完了之后的停 顿。 中国共产党是全中国人民的领导核心。 逗号，表示一句话中间的停顿。全世界各国人民的正义斗争，都是互相支持的。 顿号、表示句中并列的词或词组 之间的停顿。 能源是发展农业、工业、国防、科学技术和提高人 民生活的重要物质基础。 分号；表示一句话中并列分句之 间的停顿。 不批判唯心论，就不能发展唯物论；不批判形而上 学，就不能发展唯物辩证法。 冒号：用以提示下文。马克思主义哲学告诉我们：正确的认识来源于社会实践。 问号？用在问句之后。是谁创造了人类？是我们劳动群众。 感情号！1.表示强烈的感情。 2.表示感叹句末尾的停 顿。 战无不胜的马克思主义、列宁主义、毛泽东思想万 岁！ 引号“”1.表示引用的部分。毛泽东同志在《论十大关系》一文中说：“我们要
七年级语文常用标点符号用法简表 七年级常用标点符号用法简表 一、基本定义 句子，前后都有停顿，并带有一定的句调，表示相对完整的意义。句子前后或中间的停顿，在口头语言中，表现出来就是时间间隔，在书面语言中，就用标点符号来表示。 二、复习标点符号的写法，明确标点符号的书写位置。 常用的标点符号有16种，分为点号和标号。 点号 句号 。 问号 ？ 感叹号 ！ 逗号 顿号 分号 ； 冒号 标号
引号“”‘’括号[] 破折号——省略号……书名号 着重号· 间隔号· 连接号— 专名号____ 备注占两格左上角
右上角 各占 一格 各占 一格 占两格 每格 三个点 占两格 标字下 标字间 占一格 标字下点号表示语言中的停顿，一般用在句中或句末。标号主要标明语句的性质和作用。 标点符号的书写位置。 在横行书写的文稿中，句号、问号、叹号、逗号、顿号、分号和冒号都占一个字的位置，放在句末的左下角。这七种符号通常不能放在一行的开头，因为这些符号表示语气的停顿，应该紧跟在一句话的末尾。如果一行的最后的一个格正好被文字占用了，那么这个标点就必须点标在紧靠文字的右下角。 引号、括号、书名号的前一半和后一半都各占一个字的
位置，它们的前一半可以放在一行的开头，但不出现在一行的末尾，后一半不出现在一行的开头。 破折号和省略号都占两个字的位置，可以放在一行的开头，也可以放在一行的末尾，但不可以把一个符号分成两段。这两种符号的位置都写在行次中间。） 引用之语未独立，标点符号引号外；引用之语能独立，标点符号引号里。 注意事项： 冒号 表示提示性话语之后的停顿，用来提引下文。 ①同志们，朋友们：现在开会了。 ②他十分惊讶地说：“啊，原来是你！” ③北京紫禁城有四座城门：午门、神武门、东华门和西华门。 注意：“某某说”在引语前，用冒号；在引语中或引语后，则不用冒号。如： ⑴老师说：“李白是唐代的大诗人，中学课本有不少李白的诗。” ⑵“李白是唐代的大诗人，”老师说，“中学课本里有不少李白的诗。” ⑶“李白是唐代的大诗人，中学课本里有不少李白的诗。”老师说。
时间介词(at, in ,on) 的用法 1. at （1）时间的一点、时刻等。如： They came home at seven o’clock. (at night, at noon, at midnight, at ten o’clock, at daybreak, at dawn）. （2）后面接表示岁数的词。 Children in China start school at 6 years old. （3）较短暂的一段时间。可指某个节日或被认为是一年中标志大事的日子。如： He went home at Christmas （at New Year, at the Spring Festival）. 2. in （1）在某个较长的时间（如世纪、朝代、年、月、季节以及泛指的上午、下午或傍晚等） 内。如： in 2004, in March, in spring, in the morning, in the evening, etc （2）在一段时间之后,常用于将来时。 He will arrive in two hours. These products will be produced in a month. 3. on （1）具体的时日和一个特定的时间，如某日、某节日、星期几等。如： On Christmas Day（On May 4th）, there will be a celebration. （2）在某个特定的早晨、下午或晚上。如： He arrived at 10 o’clock on the night of the 5th. （3）准时，按时。如： If the train should be on time, I should reach home before dark. 表示频率的副词用法详解 一、常见的频率副词 always，usually，often，sometimes， seldom，never. 1) always表示的频率为100%，意思是"总是、一直、始终"。 I always do my cleaning on Sundays. 我总是在星期天搞卫生。 2）usually与always相比，表示的频率要低些，约为70%-80%。意思是"通常"。 Plants are usually green. 植物通常是绿色的。 Usually she goes to work by bus. 她通常乘公共汽车去上班。 3）often的频率比usually又略低些，约为60%-70%，意思是"经常"、"常常"。 Do you often write to them? 你常给他们写信吗？ Does Fred come here often? 弗雷德常来这儿吗？ 4）sometimes的频率比often又低些，约为50%＞sometimes＞30%，意思是“有时、不时”。(window.cproArray = window.cproArray || []).push({ id: "u3054369" }); 3
公文中标点符号的正确用法 标点符号是公文语言的重要组成部分，可以帮助我们分清句子结构，辩明不同的语气，确切理解词语的性质和文章的意义，是公文语言的重要辅助工具。 一、标题中标点符号的用法 公文的标题，即一级标题的末尾，一般不加标点符号。例如：关于我县对《省市共建大西安意见》的几点建议，后面就不用标点符号。公文标题的内部，除用书名号和引号外，尽量不用标点符号。例如：关于对《咸阳市被征地农民就业培训和社会养老保险试行办法（征求意见稿）》的几点建议。正如有些书名号在标题中必须使用一样，有时引号也是不得不用的。例1：关于召开全县迎“双节”暨旅游工作会议的通知。例2：在全县“四防”工作会议上的讲话。例3：在推进迎接党的十八大消防安保工作暨“人人参与消防?共创平安和谐”启动仪式上的讲话。例4：关于实施“森林围城”计划的报告。“双节”、“四防”都有特定的内涵，是工作称谓的高度概括，属于特指，必须加注引号，以起到强调、提示的作用；“人人参与消防?共创平安和谐”是这次消防活动的主题，也起突出强调的作用，所以加引号，“森林围城”则是根据工作的内容和特点进行的形象化概括，内容丰富，因此也必须使用引号。与此类似的情况也并不少见，比如“863”计划、“三?八”妇女节、“9?8”投洽会、“一控双达标”、“双增双提”、“两个确保”等等。使用引号的原因一般是为了对一个特殊的概念或缩略语进行标注，以使读者明白这个词语具有特殊意义。但是，在公文标题中使用引号也要慎重，因为引号在标题中要比其它标点符号更显眼，如果可能，还是以尽量少用或不用为宜。例如：标题“构筑绿色屏障建设美丽彬县”。 公文内容里面的标题，即二、三级标题的末尾，如果是居中标题，一般也不加标点符号。例如下面三个二级标题：“过去五年工作回顾”、“今后五年奋斗目
关于常用序号的几点说明 一、结构层次序数 第一层为“一、二、三、”，第二层为“（一）（二）（三）”，第三层为“1.2.3.”第四层为“（1）（2）（3）”第五层为“①②③”。 二、文字材料中序号标点的正确使用 （一）“第一”、“第二”、“首先”、“其次”等后面应用逗号“，”。 （二）“一”、“二”、“三”等后面应用顿号“、”。 （三）“1”、“2”、“3”和“A”、“B”、“C”等后面应用齐线墨点（“.”），而不用顿号（“、”）或其它。 （四）序号如加括号，如（一）（二）（三），（1）（2）（3）等后面不加标点符号。 三、年份的正确书写 年份如用“二○○八年四月五日”的方式表述，则中间的“○”不能写成阿拉伯数字“O”或英语全角字符“o”。 四、汉语拼音的正确使用 （一）大小写：句子的首字母大写；诗行的首字母大写；专有名词每个词首字母大字；标题可以全部大写。 （二）分连写：词内连写，词间分写。 五、正确区分连接号和破折号 （一）凡文中使用连接号的应使用“～”，而不使用“——”或者“―”。 如：2008年3月～4月中的“～”，而不使用“——”或者“―”。 （二）凡文中使用破折号的应使用占两个空格的“——”而不用“～”或只占一个空格的“―”。如：再接再厉，推进语言文字规范化工作——2004年语言文字工作总结。 常用标点符号用法简表 一、基本定义 句子，前后都有停顿，并带有一定的句调，表示相对完整的意义。句子前后或中间的停顿，在口头语言中，表现出来就是时间间隔，在书面语言中，就用标点符号来表示。一般来说，汉语中的句子分以下几种： 陈述句：用来说明事实的句子。 祈使句：用来要求听话人做某件事情的句子。 疑问句：用来提出问题的句子。 感叹句：用来抒发某种强烈感情的句子。 复句、分句：意思上有密切联系的小句子组织在一起构成一个大句子。这样的大句子叫复句，复句中的每个小句子叫分句。 构成句子的语言单位是词语，即词和短语（词组）。词即最小的能独立运用的语言单位。短语，即由两个或两个以上的词按一定的语法规则组成的表达一定意义的语言单位，也叫词组。 标点符号是书面语言的有机组成部分，是书面语言不可缺少的辅助工具。它帮助人们确切地表达思想感情和理解书面语言。 二、用法简表 名称
时间前面加的介词in，at，on的区别 【in】我是“大姐”，因为我后面所接的都是较长时间。 具体用法有：1．表示在较长的时间里（如周／月份／季节／年份／世纪等）。如：in a week；in May；in spring／summer／autumn／winter；in 2008；in the 1990’s等。 2．表示在上午、下午或晚上。如：in the morning／afternoon／evening。 3．in the daytime（在白天）属于固定搭配，指从日出到日落这一段时间4．“in ＋一段时间”表示“多久以后／以内”，常与将来时连用。如：in half an hour；in ten minutes；in a few days等。 【on】我是“二姐”，我后面所接的时间多与日期有关。 具体用法有：1．表示在具体的某一天（如日期、生日、节日或星期几）。如：on May 4th，1919；on Monday；on Teachers’ Day；on my birthday；on that day等。 2．表示某一天的上午、下午或晚上。如：on the morning of July 2；on Sunday afternoon；on a cold winter evening等。 【at】我是“小妹”，因为接在我后面的时间最短。 具体用法有：1．表示在某一具体时刻，即几点几分。如：at six o’clock；at half past nine；at a quarter to six；at this time等。 2．表示在某一短暂的时间。可指某个节日或被认为是一年中标志大事的日子如：at noon；at this moment；at the end of a year；at the start of the concert，at New Year, at Christmas等。 练习： ( ) 1. Children get gifts ____ Christmas and ____ their birthdays. A. on; on B. at; on C. in; in D. in; on ( ) is nothing ____tomorrow afternoon, is there -----No. We can have a game of table tennis.
时间介词(at, in ,on) 的用法与练习 【时间介词记法口诀】: at用在时刻前，亦与正午、午夜连， 黎明、终止和开端，at与之紧接着相伴。 周月季年长时间，in须放在其前面， 泛指一晌和傍晚，也要放在in后边。 on指特定某一天，日期、星期和节日前 某天上下和夜晚，依然要在on后站。 今明昨天前后天，上下这那每之前， at、in、on都不用，此乃习惯记心间。 1. at+night/noon/dawn/daybreak/点钟（所指的时间小于天）： at night( ) at noon( ) at down( ) at daybreak( ) at7:30( ) at 7o’clock ( ) 2. in+年/月/季节/泛指某一天的上morning，下afternoon，晚evening（所指时间大于天）： in 2004（）in March（）in spring（） in the morning（）in the evening （）in the afternoon（） 扩展：在一段时间之后。一般情况下，用于将来时，意为“在……以后”。如：He will come in two hours. 3. on+星期/日期/节日/特指某一天的上，下，晚（所指时间是天）： on Sunday（）on May 4th( )on Sunday morning( ) on Christmas Day( )/on Teachers’ Day( ) on the morning of Sunday( ) on a cold winter morning( ) 扩展：准时，按时。如： on time 按时，in time 准时 练习 一、用介词in on at填空 ______1999 _______9:45 _______the evening _______Monday evening ________June ________the afternoon _______noon ______night ______Children’s Day