Treatment of Epsilon Moves in Subset Construction
Gertjan van Noord?
Rijksuniversiteit Groningen
The paper discusses the problem of determinising?nite-state automata containing large numbers of -moves.Experiments with?nite-state approximations of natural language grammars often give rise to very large automata with a very large number of -moves. The paper identi?es and compares a number of subset construction algorithms which treat -moves.Experiments have been performed which indicate that the algorithms differ con-siderably in practice,both with respect to the size of the resulting deterministic automaton, and with respect to practical ef?ciency.Furthermore,the experiments suggest that the av-erage number of -moves per state can be used to predict which algorithm is likely to be the fastest for a given input automaton.
1Introduction
1.1Finite-state Language Processing
An important problem in computational linguistics is posed by the fact that the grammars which are typically hypothesised by linguists are unattractive from the point of view of computation.For instance,the number of steps required to anal-yse a sentence of n words is n3for context-free grammars.For certain linguistically more attractive grammatical formalisms it can be shown that no upper-bound to the number of steps required to?nd an analysis can be given.The human lan-guage user,however,seems to process in linear time;humans understand longer sentences with no noticeable delay.This implies that neither context-free gram-mars nor more powerful grammatical formalisms are likely models for human lan-guage processing.An important issue therefore is how the linearity of processing by humans can be accounted for.
A potential solution to this problem concerns the possibility of approximating an underlying general and abstract grammar by techniques of a much simpler sort. The idea that a competence grammar might be approximated by?nite-state means goes back to early work by Chomsky(Chomsky,1963;Chomsky,1964).There are essentially three observations which motivate the view that the processing of nat-ural language is?nite-state:
1.humans have a?nite(small,limited,?xed)amount of memory available
for language processing
2.humans have problems with certain grammatical constructions,such as
center-embedding,which are impossible to describe by?nite-state means
(Miller and Chomsky,1963)
3.humans process natural language very ef?ciently(in linear time)
1.2Finite-state Approximation and -moves
In experimenting with?nite-state approximation techniques for context-free and more powerful grammatical formalisms(such as the techniques presented in Black (1989),Pereira and Wright(1991),Rood(1996),Pereira and Wright(1997),Evans (1997),Nederhof(1997),Nederhof(1998),Johnson(1998))we have found that the resulting automata often are extremely large.Moreover,the automata contain many -moves(jumps).And?nally,if such automata are determinised then the re-sulting automata are often smaller.It turns out that a straightforward implementa-tion of the subset construction determinisation algorithm performs badly for such inputs.In this paper we consider a number of variants of the subset-construction algorithm which differ in their treatment of -moves.
Although we have observed that?nite-state approximation techniques typi-cally yield automata with large amounts of -moves,this is obviously not a ne-cessity.Instead of trying to improve upon determinisation techniques for such au-tomata it might be more fruitful,perhaps,to try to improve these approximation techniques in such a way that more compact automata are produced.1However, because research into?nite-state approximation is still of an exploratory and ex-perimental nature,it can be argued that more robust determinisation algorithms do still have a role to play:it can be expected that approximation techniques are much easier to de?ne and implement if the resulting automaton is allowed to be non-deterministic and to contain -moves.
Note furthermore that even if our primary motivation is in?nite-state approx-imation,the problem of determinising?nite-state automata with -moves may be relevant in other areas of language research as well.
1.3Subset construction and -moves
The experiments were performed using the FSA Utilities.The FSA Utilities tool-box(van Noord,1997;van Noord,1999;Gerdemann and van Noord, 1999;van Noord and Gerdemann,1999)is a collection of tools to manipu-late regular expressions,?nite-state automata and?nite-state transducers.Ma-nipulations include determinisation,minimisation,composition,complementa-tion,intersection,Kleene closure,etc.Various visualisation tools are available to browse?nite-state automata.The tool-box is implemented in SICStus Pro-log,and is available free of charge under Gnu General Public License via anonymous ftp at ftp://ftp.let.rug.nl/pub/vannoord/Fsa/,and via the web at http://www.let.rug.nl/?vannoord/Fsa/.At the time of our initial experiments with?nite-state approximation,an old version of the tool-box was used,which ran into memory problems for some of these automata.For this reason,the sub-set construction algorithm has been re-implemented,paying special attention to the treatment of -moves.Three variants of the subset construction algorithm are identi?ed which differ in the way -moves are treated:
per graph The most obvious and straightforward approach is sequential in the fol-lowing sense.Firstly,an equivalent automaton without -moves is con-
structed for the input.In order to do this,the transitive closure of the graph
consisting of all -moves is computed.Secondly,the resulting automaton
is then treated by a subset construction algorithm for -free automata.Dif-
ferent variants of per graph can be identi?ed,depending on the implemen-
tation of the -removal step.
per state For each state which occurs in a subset produced during subset construc-
tion,compute the states which are reachable using -moves.The results of
this computation can be memorised,or computed for each state in a pre-processing step.This is the approach mentioned brie?y in Johnson and
Wood(1997).2
per subset For each subset Q of states which arises during subset construction, compute Q ?Q which extends Q with all states which are reachable from
any member of Q using -moves.Such an algorithm is described in Aho,
Sethi,and Ullman(1986).
The motivation for this paper is the experience that the?rst approach turns out to be impractical for automata with very large numbers of -moves.An integration of the subset construction algorithm with the computation of -reachable states performs much better in practice for such automata.
Section2presents a short statement of the problem(how to determinise a given?nite-state automaton),and a subset construction algorithm which solves this problem in the absence of -moves.Section3de?nes a number of subset con-struction algorithms which differ with respect to the treatment of -moves.Most aspects of the algorithms are not new and have been described elsewhere,and/or were incorporated in previous implementations;a comparison of the different al-gorithms had not been performed previously.We provide a comparison with re-spect to the size of the resulting deterministic automaton(in section3)and prac-tical ef?ciency(in section4).Section4provides experimental results both for ran-domly generated automata and for automata generated by approximation algo-rithms.Our implementations of the various algorithms are also compared with AT&T’s FSM utilities(Mohri,Pereira,and Riley,1998),to establish that the experi-mental differences we?nd between the algorithms are truly caused by differences in the algorithm(as opposed to accidental implementation details).
2Subset Construction
2.1Problem statement
Let a?nite-state machine M be speci?ed by a tuple(Q,Σ,δ,S,F)where Q is a ?nite set of states,Σis a?nite alphabet,δis a function from Q×(Σ∪{ })→2Q. Furthermore,S?Q is a set of start states and F?Q is a set of?nal states.3 Let -move be the relation{(q i,q j)|q j∈δ(q i, )}. -reachable is the re?exive and transitive closure of -move.Let -CLOSURE:2Q→2Q be a function which is de?ned as:
-CLOSURE(Q )={q|q ∈Q ,(q ,q)∈ -reachable} Furthermore,we write -CLOSURE?1(Q )for the set{q|q ∈Q ,(q,q )∈ -reachable}.
For any given?nite-state automaton M=(Q,Σ,δ,S,F)there is an equivalent deterministic automaton M =(2Q,Σ,δ ,{Q0},F ).F is the set of all states in2Q containing a?nal state of M,i.e.,the set of subsets{Q i∈2Q|q∈Q i,q∈F}.M has a single start state Q0which is the epsilon closure of the start states of M,i.e., Q0= -CLOSURE(S).Finally,
δ ({q1,q2,...,q i},a)= -CLOSURE(δ(q1,a)∪δ(q2,a)∪...∪δ(q i,a))
funct construction((Q,Σ,δ,S,F))
index
closure(S)
add(Start)
while
mark(T)
foreach
U:=epsilon
od
(States,Σ,Trans,{Start},Finals)
end
add(U)Reachable-state-set Maintenance
if
add U unmarked to States
if Finals:=Finals∪{U}?
end
instructions(P)Instruction Computation
return
funct closure(U)variant1:No -moves
return
Figure1
Subset-construction algorithm.
An algorithm which computes M for a given M will only need to take into account states in2Q which are reachable from the start state Q0.This is the reason that for many input automata the algorithm does not need to treat all subsets of states(but note that there are automata for which all subsets are relevant,and hence exponential behaviour cannot be avoided in general).
Consider the subset construction algorithm in?gure1.The algorithm main-tains a set of subsets States.Each subset can be either marked or unmarked(to in-dicate whether the subset has been treated by the algorithm);the set of unmarked subsets is sometimes referred to as the agenda.The algorithm takes such an un-marked subset T and computes all transitions leaving T.This computation is per-formed by the function instructions and is called instruction computation by Johnson and Wood(1997).
The function index
The procedure add is responsible for‘reachable-state-set maintenance’,by en-suring that target subsets are added to the set of subsets if these subsets were not encountered before.Moreover,if such a new subset contains a?nal state,then this subset is added to the set of?nal states.
3Variants for -Moves
The algorithm presented in the previous section does not treat -moves.In this section,possible extensions of the algorithm are identi?ed to treat -moves.
3.1Per graph
In the per graph variant two steps can be identi?ed.In the?rst step,efree,an equiv-alent -free automaton is constructed.In the second step this -free automaton is determinised using the subset construction algorithm.The advantage of this ap-proach is that the subset construction algorithm can remain simple because the input automaton is -free.
An algorithm for efree is described for instance in Hopcroft and Ullman (1979)[page26-27].The main ingredient of efree is the construction of the func-tion -CLOSURE,which can be computed by using a standard transitive closure algorithm for directed graphs:this algorithm is applied to the directed graph con-sisting of all -moves of M.Such an algorithm can be found in several textbooks (see,for instance,Cormen,Leiserson,and Rivest(1990)).
For a given?nite-state automaton M=(Q,Σ,δ,S,F)efree computes M = (Q,Σ,δ ,S ,F ),where S = -CLOSURE(S),F = -CLOSURE?1(F),and δ (p,a)={q|q ∈δ(p ,a),p ∈ -CLOSURE?1(p),q∈ -CLOSURE(q )}.Instead of using -CLOSURE on both the source and target side of a transition,efree can be optimised in two different ways by using -CLOSURE only on one side:?efree t:M =(Q,Σ,δ ,S ,F),where S = -CLOSURE(S),and
δ (p,a)={q|q ∈δ(p,a),q∈ -CLOSURE(q )}.
?efree s:M =(Q,Σ,δ ,S,F ),where F = -CLOSURE?1(F),and
δ (p,a)={q|q∈δ(p ,a),p ∈ -CLOSURE?1(p)}.
Although both variants appear very similar,there are some differences.Firstly, efree t might introduce states which are not co-accessible:states from which no path exists to a?nal state;in contrast,efree s might introduce states which are not acces-sible:states from which no path exists from the start state.A straightforward mod-i?cation of both algorithms is possible to ensure that these states are not present in the output.Thus efree t,c ensures that all states in the resulting automaton are co-accessible;efree s,a ensures that all states in the resulting automaton are accessi-ble.As a consequence,the size of the determinised machine is in general smaller if efree t,c is employed,because states which were not co-accessible(in the input) are removed(this is therefore an additional bene?t of efree t,c;the fact that efree s,a removes accessible states has no effect on the size of the determinised machine because the subset construction algorithm already ensures accessibility anyway).
Secondly,it turns out that applying efree t in combination with the subset-construction algorithm generally produces smaller automata than efree s(even if we ignore the bene?t of ensuring co-accessibility).An example is presented in?g-ure2.The differences can be quite signi?cant.This is illustrated in?gure3.
Below we will write per graph X to indicate the non-integrated algorithm based on efree X.
(1)
a
(2)
a
a
a
Figure 2
Illustration of the difference in size between two variants of efree .(1)is the input
automaton.The result of efree t is given in (2);(3)is the result of efree s .(4)and (5)are the result of applying the subset construction to the result of efree t and efree s ,respectively.
020004000600080001000012000140001600018000200000
0.2
0.4
0.60.81 1.2 1.4 1.6
1.8
2
N u m b e r o f S t a t e s
Deterministic Jump Density (mean)
efree-source efree-target
Figure 3
Difference in sizes of deterministic automata constructed with either efree s or efree t ,for randomly generated input automata consisting of 100states,15symbols,and various numbers of transitions and jumps (cf.section 4).Note that all states in the input are co-accessible;the difference in size is due solely to the effect illustrated in ?gure 2.
funct
t∈T do
while
mark(t)
foreach
if add q unmarked to D?
od
D
end
closure function.
The approach in which the transitive closure is computed for one state at a time is de?ned by the following de?nition of the epsilon
epsilon
memo(
u∈U
memo(closure({u})))
end
epsilon
4This is an improvement over the algorithm given in a preliminary version of this paper(van Noord,1998).
return
The motivation for the per state variant is the insight that in this case the closure algorithm is called at most|Q|times.In contrast,in the per subset approach the transitive closure algorithm may need to be called2|Q|times.On the other hand, in the per state approach some overhead must be accepted for computing the union of the results for each state.Moreover,in practice the number of subsets is often much smaller than2|Q|.In some cases,the number of reachable subsets is smaller than the number of states encountered in those subsets.
3.3Implementation
In order to implement the algorithms ef?ciently in Prolog,it is important to use ef-?cient data-structures.In particular,we use an implementation of(non-updatable) arrays based on the N+K trees of O’Keefe(1990,pp.142-145)with N=95and K=32. On top of this datastructure,a hash array is implemented using the SICStus library predicate term
5All the automata used in the experiments are freely available from
http://www.let.rug.nl/?vannoord/Fsa/.
6Leslie uses the terms absolute density and deterministic density.
1
10
100
1000
10000
100000
1e+061
10
C P U -t i m e (m s e c ) / N u m b e r o f S t a t e s (i n p u t +o u t p u t )
Deterministic Density
fsa fsm states
Figure 5
Deterministic transition density versus CPU-time in msec.The input automata have 25states,15symbols,and no -moves.fsa represents the CPU-time required by our FSA6implementation;fsm represents the CPU-time required by AT&T’s FSM library;states represents the sum of the number of states of the input and output automata.
tion time,and the maximum number of states,at an approximate
deterministic density of2.Most of the area under the curve occurs
within0.5and2.5deterministic density—this is the area in which
subset construction is expensive.
Conjecture.For a given NFA,we can compute the expected num-
bers of states and transitions in the corresponding DFA,produced
by subset construction,from the deterministic density of the NFA.
In addition,this functional relationship gives rise to a Poisson-like
curve with its peak approximately at a deterministic density of2.
A number of automata were generated randomly,according to the number of states,symbols,and transition density.For the?rst experiment,automata were generated consisting of15symbols,25states,and various densities(and no -moves).The results are summarised in?gure5.CPU-time was measured on a HP 9000/785machine running HP-UX10.20.Note that our timings do not include the start-up of the Prolog engine,nor the time required for garbage collection.
In order to establish that the differences we obtain later are genuinely due to differences in the underlying algorithm,and not due to‘accidental’implemen-tation details,we have compared our implementation with the determiniser of AT&T’s FSM utilities(Mohri,Pereira,and Riley,1998).For automata without -moves we establish that FSM normally is faster:for automata with very small tran-sition densities FSM is up to four times as fast,for automata with larger densities the results are similar.
A new concept called absolute jump density is introduced to specify the num-ber of -moves.It is de?ned as the number of -moves divided by the square of the number of states(i.e.,the probability that an -move exists for a given pair of states).Furthermore,deterministic jump density is the number of -moves divided by the number of states(i.e.,the average number of -moves which leave a given state).In order to measure the differences between the three implementations,a number of automata has been generated consisting of15states and15symbols, using various transition densities between0.01and0.3(for larger densities the automata tend to collapse to an automaton forΣ?).For each of these transition densities,deterministic jump densities were chosen in the range0to2.5(again,for larger values the automata tend to collapse).In?gures6to9the outcomes of these experiments are summarised by listing the average amount of CPU-time required per deterministic jump density(for each of the algorithms),using automata with 15,20,25and100states respectively.Thus,every dot represents the average for de-terminising a number of different input automata with various absolute transition densities and the same deterministic jump density.
The striking aspect of these experiments is that the integrated per subset and per state variants are much more ef?cient for larger deterministic jump density.The per graph t is typically the fastest algorithm of the non-integrated versions.However, in these experiments all states in the input are co-accessible by construction;and moreover,all states in the input are?nal states.Therefore,the advantages of the per graph t,c algorithm could not be observed here.
The turning point is around a deterministic jump density of around0.8:for smaller densities the per graph t is typically slightly faster;for larger densities the per state algorithm is much faster.For densities beyond1.5,the per subset algorithm tends to perform better than the per state algorithm.Interestingly,this generalisa-tion is supported by the experiments on automata which were generated by ap-proximation techniques(although the results for randomly generated automata are more consistent than the results for‘real’examples).
10
100
1000
10000
00.51
1.5
2 2.5
M e a n C P U -t i m e (m s e c )
#Jumps/#States
per_graph(t)per_graph(s)per_graph(s,a)per_graph(t,c)
per_subset per_state
fsm
Figure 6
Average amount of CPU-time versus jump density for each of the algorithms,and FSM.Input automata have 15states.Absolute transition densities:0.01-0.3.
10
100
1000
10000
00.51
1.5
2 2.5
M e a n C P U -t i m e (m s e c )
#Jumps/#States
per_graph(t)per_graph(s)per_graph(s,a)per_graph(t,c)
per_subset per_state
fsm
Figure 7
Average amount of CPU-time versus jump density for each of the algorithms,and FSM.Input automata have 20states.Absolute transition densities:0.01-0.3.
10
100
1000
10000100000
00.51
1.5
2 2.5
M e a n C P U -t i m e (m s e c )
#Jumps/#States
per_graph(t)per_graph(s)per_graph(s,a)per_graph(t,c)
per_subset per_state
fsm
Figure 8
Average amount of CPU-time versus deterministic jump density for each of the algorithms,and FSM.Input automata have 25states.Absolute transition densities:0.01-0.3.
100
1000
10000
100000
00.51
1.5
2 2.5
M e a n C P U -t i m e (m s e c )
#Jumps/#States
per_graph(t)per_graph(s)per_graph(s,a)per_graph(t,c)
per_subset per_state
fsm
Figure 9
Average amount of CPU-time versus deterministic jump density for each of the algorithms,and FSM.Input automata have 100states.Absolute transition densities:0.001-0.0035.
Comparison with the FSM library We also provide the results for AT&T’s FSM li-brary again.FSM is designed to treat weighted automata for very general weight sets.The initial implementation of the library consisted of an on-the-?y computa-tion of the epsilon-closures combined with determinisation.This was abandoned for two reasons:it could not be generalised to the case of general weight sets,and it was not outputting the intermediate epsilon-removed machine(which might be of interest in itself).In the current version -moves must be removed before deter-minisation is possible.This mechanism thus is comparable to our per graph variant. Apparently,FSM employs an algorithm equivalent to our per graph s,a.The result-ing determinised machines are generally larger than the machines produced by our integrated variants and the variants which incorporate -moves on the target side of transitions.The timings below are obtained for the pipe
fsmrmepsilon|fsmdeterminize
This is somewhat unfair since this includes the time to write and read the interme-diate machine.Even so,it is interesting to note that the FSM library is a constant factor faster than our per graph s,a;for larger numbers of jumps the per state and per subset variants consistently beat the FSM library.
Experiment:Automata generated by approximation algorithms The automata used in the previous experiments were randomly generated.However,it may well be that in practice the automata that are to be treated by the algorithm have typical proper-ties which were not re?ected in this test data.For this reason results are presented for a number of automata that were generated using approximation techniques for context-free grammars.In particular,automata have been used which were cre-ated by Nederhof,using the technique described in Nederhof(1997).In addition,a small number of automata have been used which were created using the technique of Pereira and Wright(1997)(as implemented by Nederhof).We have restricted our attention to automata with at least1000states in the input.
The automata typically contain lots of jumps.Moreover,the number of states of the resulting automaton is often smaller than the number of states in the input au-tomaton.Results are given in the tables1and2.One of the most striking examples is the ygrim automaton consisting of3382states and9124jumps.For this example, the per graph implementations ran out of memory(after a long time),whereas the implementation of the per subset algorithm produced the determinised automaton (containing only9states)within a single CPU-second.The FSM implementation took much longer for this example(whereas for many of the other examples it is faster than our implementations).Note that this example has the highest number of jumps per number of states ratio.This con?rms the observation that the per sub-set algorithm performs better on inputs with a high deterministic jump density.
5Conclusion
We have discussed a number of variants of the subset-construction algorithm for determinising?nite automata containing -moves.The experiments support the following conclusions:
?The integrated variants per subset and per state work much better for
automata containing a large number of -moves.The per subset variant
tends to improve upon the per state algorithm if the number of -moves
increases even further.
?We have identi?ed four different variants of the per graph algorithm.In
our experiments,the per graph t is the algorithm of choice for automata
Input Output
#states#jumps#states
per graph t
per subset
per state
g14403137131 ovis4.n2210164107 g131006337329 rene22597846844 ovis9.p279124781386 ygrim542299 ygrim.p63704702702 java192833319711855 java164393531863078 zovis37889551744182 zovis28040065615309 Table1
The automata generated by approximation algorithms.The table lists the number of states, transitions and jumps of the input automaton,and the number of states of the determinised machine using respectively the efree s,efree t,and the efree t,c variants.
CPU-time(sec)
graph t graph s subset FSM
0.40.30.40.1
ovis4.n 1.1 1.00.6
0.90.6 1.20.2
rene20.30.20.2
36.616.925.221.9
ygrim--21.0
--562.14512.4 java1967.445.019.0
30.035.011.3 3.0
zovis3557.5407.4302.5
909.2-454.4392.1 Table2
Results for automata generated by approximation algorithms.The dashes in the table indicate that the corresponding algorithm ran out of memory(after a long period of time) for that particular example.
containing few moves,because it is faster than the other algorithms,and
because it produces smaller automata than the per graph s and per graph s,a
variants.
?The per graph t,c variant is an interesting alternative in that it produces the
smallest results.This variant should be used if the input automaton is
expected to contain many non-co-accessible states.
?Automata produced by?nite-state approximation techniques tend to
contain many -moves.We found that for these automata the differences
in speed between the various algorithms can be enormous.The per subset
and per state algorithms are good candidates for this application.
We have attempted to characterize the expected ef?ciency of the various al-gorithms in terms of the number of jumps and the number of states in the input automaton.It is quite conceivable that other simple properties of the input automa-ton can be used even more effectively for this purpose.One reviewer suggests to use the number of strongly -connected components(the strongly connected com-ponents of the graph of all -moves)for this purpose.We leave this and other pos-sibilities to a future occasion.
Acknowledgments
I am grateful to Mark-Jan Nederhof for support,and for providing me with lots of (often dreadful)automata generated by his?nite-state approximation tools.The comments of the anonymous FSMNLP and CL reviewers were extremely useful.
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五年级上册成语解释及近义词反义词和造句大全 囫囵吞枣；【解释】：囫囵：整个儿。把枣整个咽下去，不加咀嚼，不辨味道。比喻对事物不加分析考虑。【近义词】：不求甚解【反义词】融会贯穿[造句];学习不能囫囵吞枣而是要精益求精 不求甚解；bùqiúshènjiě【解释】：甚：专门，极。只求明白个大概，不求完全了解。常指学习或研究不认真、不深入【近义词】：囫囵吞枣【反义词】：精益求精 造句；1；在学习上，我们要理解透彻，不能不求甚解 2；学习科学文化知识要刻苦钻研，深入领会，不能粗枝大叶，不求甚解。 千篇一律；【解释】：一千篇文章都一个样。指文章公式化。也比喻办事按一个格式，专门机械。 【近义词】：千人一面、如出一辙【反义词】：千差万别、形形色色 造句；学生旳作文千篇一律，专门少能有篇与众不同旳，这确实是平常旳练习太少了。 倾盆大雨；qīngpéndàyǔ【解释】：雨大得象盆里旳水直往下倒。形容雨大势急。 【近义词】：大雨如柱、大雨滂沱【反义词】：细雨霏霏牛毛细雨 造句；3月旳天说变就变，瞬间下了一场倾盆大雨。今天下了一场倾盆大雨。 坚决果断；áobùyóuyù：意思；做事果断，专门快拿定了主意，一点都不迟疑，形容态度坚决 近义词；不假思索斩钉截铁反义词；犹豫不决 造句；1看到小朋友落水，司马光坚决果断地搬起石头砸缸。2我坚决果断旳承诺了她旳要求。 饥肠辘辘jīchánglùlù【近义词】：饥不择食【反义词】：丰衣足食 造句；1我放学回家已是饥肠辘辘。2那个饥肠辘辘旳小孩差不多两天没吃饭了 滚瓜烂熟gǔnguālànshóu〔shú)【解释】：象从瓜蔓上掉下来旳瓜那样熟。形容读书或背书流利纯熟。【近义词】：倒背如流【反义词】：半生半熟造句；1、这篇课文我们早已背得滚瓜烂熟了 流光溢彩【liúguāngyìcǎi】解释；光影，满溢旳色彩，形容色彩明媚 造句：国庆节，商场里装饰旳流光溢彩。 津津有味；jīnjīnyǒuwèi解释：兴趣浓厚旳模样。指吃得专门有味道或谈得专门有兴趣。 【近义词】：兴致勃勃有滋有味【反义词】：索然无味、枯燥无味 造句；1今天旳晚餐真丰富，小明吃得津津有味。 天长日久；tiānchángrìjiǔ【解释】：时刻长，生活久。【近义词】：天长地久【反义词】：稍纵即逝 造句：小缺点假如不立即改掉, 天长日久就会变成坏适应 如醉如痴rúzuìrúchī【解释】：形容神态失常，失去自制。【近义词】：如梦如醉【反义词】：恍然大悟造句；这么美妙旳音乐，我听得如醉如痴。 浮想联翩【fúxiǎngliánpiān解释】：浮想：飘浮不定旳想象；联翩：鸟飞旳模样，比喻连续不断。指许许多多旳想象不断涌现出来。【近义词】：思绪万千 造句；1他旳话让人浮想联翩。2:这幅画饱含诗情，使人浮想联翩，神游画外，得到美旳享受。 悲欢离合bēihuānlíhé解释；欢乐、离散、聚会。泛指生活中经历旳各种境遇和由此产生旳各种心情【近义词】：酸甜苦辣、喜怒哀乐【反义词】：平淡无奇 造句；1人一辈子即是悲欢离合,总要笑口常开,我们旳生活才阳光明媚. 牵肠挂肚qiānchángguàdù【解释】：牵：拉。形容十分惦念，放心不下 造句；儿行千里母担忧，母亲总是那个为你牵肠挂肚旳人 如饥似渴rújīsìkě：形容要求专门迫切，仿佛饿了急着要吃饭，渴了急着要喝水一样。 造句；我如饥似渴地一口气读完这篇文章。他对知识旳如饥似渴旳态度造就了他今天旳成功。 不言而喻bùyánéryù【解释】：喻：了解，明白。不用说话就能明白。形容道理专门明显。 【近义词】：显而易见【反义词】：扑朔迷离造句；1珍惜时刻，好好学习，那个道理是不言而喻旳 与众不同；yǔzhòngbùtóng【解释】：跟大伙不一样。 〖近义词〗别出心裁〖反义词〗平淡无奇。造句； 1从他与众不同旳解题思路中，看出他专门聪慧。2他是个与众不同旳小孩
The way 的用法 Ⅰ常见用法： 1)the way+ that 2)the way + in which（最为正式的用法） 3)the way + 省略（最为自然的用法） 举例：I like the way in which he talks. I like the way that he talks. I like the way he talks. Ⅱ习惯用法： 在当代美国英语中，the way用作为副词的对格，“the way+ 从句”实际上相当于一个状语从句来修饰整个句子。 1)The way =as I am talking to you just the way I’d talk to my own child. He did not do it the way his friends did. Most fruits are naturally sweet and we can eat them just the way they are—all we have to do is to clean and peel them. 2）The way= according to the way/ judging from the way The way you answer the question, you are an excellent student. The way most people look at you, you’d think trash man is a monster. 3）The way =how/ how much No one can imagine the way he missed her. 4）The way =because
1．判别下列级数的敛散性，若收敛，是条件收敛还是绝对收敛？ ⑴ 1 1 (1)n n ∞ -=-∑； 【解】级数 1 1 (1)n n ∞ -=-∑属于交错级数， 它满足关系1n n u u += >=（1,2,3,n =L ）且lim 0n n n u →∞==， 即由莱布尼兹定理知，级数 1 1 (1)n n ∞ -=-∑收敛， 但 1 1 (1) n n ∞ -=- ∑1n ∞ ==是112p =<的P 级数，发散， 综上知，级数 1 (1)n n ∞ -=-∑条件收敛。 ⑵ 1 11 (1) 3 n n n n ∞ --=-∑； 【解】级数 1 1 1(1)3n n n n ∞ --=-∑属于交错级数， 由于 1 11 (1) 3n n n n ∞ --=-∑1 13n n n ∞ -==∑， 因为111113lim lim lim 1333 n n n n n n n n u n n u n +→∞→∞→∞-++==<， 由正项级数的比值判别法知，级数 11 3n n n ∞ -=∑收敛， 综上知，级数 1 1 1 (1)3n n n n ∞ --=-∑绝对收敛。 ⑶ 1 1 ln (1)n n n n ∞ -=-∑； 【解】级数 1 1 ln (1)n n n n ∞ -=-∑属于交错级数，
由于函数ln x y x =有2 1ln '0x y x -=>当x e >时恒成立， 知ln x y x = 当x e >时为增函数， 从而满足关系1n n u u +>（3,4,5,n =L ）且1 ln lim lim lim 01 n n n n n n u n →∞→∞→∞===， 即由莱布尼兹定理知，级数 1 1 ln (1) n n n n ∞ -=-∑收敛， 但由于 1 1 ln (1) n n n n ∞ -=-∑1ln n n n ∞==∑11n n ∞=>∑，而11 n n ∞ =∑为调和级数，发散， 综上知级数 1 1 ln (1) n n n n ∞ -=-∑条件收敛。 ⑷ 1 1 1 (1)ln(1) n n n ∞ -=-+∑； 【解】级数 1 1 1 (1)ln(1) n n n ∞ -=-+∑属于交错级数， 它满足关系111 ln(1)ln(2) n n u u n n += >=++（1,2,3,n =L ） 且1 lim lim 0ln(1) n n n u n →∞ →∞==+， 即由莱布尼兹定理知，级数 1 1 1 (1)ln(1) n n n ∞ -=-+∑收敛， 但由于1lim n n n u u +→∞1 ln(1) lim 11n n n →∞+=+1lim ln(1)n n n →∞+=+1lim 1 1 n n →∞=+lim(1)n n →∞=+=∞， 且级数111n n ∞ =+∑21 n n ∞ ==∑为调和级数，发散， 即由比较判别法的极限形式知，级数 1 1 ln(1)n n ∞ =+∑发散， 综上知，级数 1 1 1 (1)ln(1) n n n ∞ -=-+∑条件收敛。
悲惨的近义词反义词和造句 导读：悲惨的近义词 悲凉(注释:悲哀凄凉：～激越的琴声。) 悲惨的反义词 幸福(注释:个人由于理想的实现或接近而引起的一种内心满足。追求幸福是人们的普遍愿望，但剥削阶级把个人幸福看得高于一切，并把个人幸福建立在被剥削阶级的痛苦之上。无产阶级则把争取广大人民的幸福和实现全人类的解放看作最大的幸福。认为幸福不仅包括物质生活，也包括精神生活;个人幸福依赖集体幸福，集体幸福高于个人幸福;幸福不仅在于享受，而主要在于劳动和创造。) 悲惨造句 1.一个人要发现卓有成效的真理，需要千百个人在失败的探索和悲惨的错误中毁掉自己的生命。 2.贝多芬的童年尽管如是悲惨，他对这个时代和消磨这时代的地方，永远保持着一种温柔而凄凉的回忆。 3.卖火柴的小女孩在大年夜里冻死了，那情景十分悲惨。 4.他相信，他们每个人背后都有一个悲惨的故事。 5.在那次悲惨的经历之后，我深信自己绝对不是那种可以离家很远的人。 6.在人生的海洋上，最痛快的事是独断独航，但最悲惨的却是回头无岸。 7.人生是艰苦的。对不甘于平庸凡俗的人那是一场无日无夜的斗
争，往往是悲惨的、没有光华的、没有幸福的，在孤独与静寂中展开的斗争。……他们只能依靠自己，可是有时连最强的人都不免于在苦难中蹉跎。罗曼·罗兰 8.伟大的心胸，应该表现出这样的气概用笑脸来迎接悲惨的厄运，用百倍的勇气来应付开始的不幸。鲁迅人在逆境里比在在顺境里更能坚强不屈。遇厄运时比交好运时容易保全身心。 9.要抓紧时间赶快生活，因为一场莫名其妙的疾病，或者一个意外的悲惨事件，都会使生命中断。奥斯特洛夫斯基。 10.在我一生中最悲惨的一个时期，我曾经有过那类的想法：去年夏天在我回到这儿附近的地方时，这想法还缠着我;可是只有她自己的亲自说明才能使我再接受这可怕的想法。 11.他们说一个悲惨的故事是悲剧，但一千个这样的故事就只是一个统计了。 12.不要向诱惑屈服，而浪费时间去阅读别人悲惨的详细新闻。 13.那起悲惨的事件深深地铭刻在我的记忆中。 14.伟大的心胸，应该用笑脸来迎接悲惨的厄运，用百倍的勇气来应付一切的不幸。 15.一个人要发现卓有成效的真理，需要千百万个人在失败的探索和悲惨的错误中毁掉自己的生命。门捷列夫 16.生活需要爱，没有爱，那些受灾的人们生活将永远悲惨;生活需要爱，爱就像调味料，使生活这道菜充满滋味;生活需要爱，爱让生活永远充满光明。
The way的用法及其含义（二） 二、the way在句中的语法作用 the way在句中可以作主语、宾语或表语： 1.作主语 The way you are doing it is completely crazy.你这个干法简直发疯。 The way she puts on that accent really irritates me. 她故意操那种口音的样子实在令我恼火。The way she behaved towards him was utterly ruthless. 她对待他真是无情至极。 Words are important, but the way a person stands, folds his or her arms or moves his or her hands can also give us information about his or her feelings. 言语固然重要,但人的站姿,抱臂的方式和手势也回告诉我们他(她)的情感。 2.作宾语 I hate the way she stared at me.我讨厌她盯我看的样子。 We like the way that her hair hangs down.我们喜欢她的头发笔直地垂下来。 You could tell she was foreign by the way she was dressed. 从她的穿著就可以看出她是外国人。 She could not hide her amusement at the way he was dancing. 她见他跳舞的姿势，忍俊不禁。 3.作表语 This is the way the accident happened.这就是事故如何发生的。 Believe it or not, that's the way it is. 信不信由你, 反正事情就是这样。 That's the way I look at it, too. 我也是这么想。 That was the way minority nationalities were treated in old China. 那就是少数民族在旧中
定冠词the的用法： 定冠词the与指示代词this ,that同源,有“那(这)个”的意思,但较弱,可以和一个名词连用,来表示某个或某些特定的人或东西. (1)特指双方都明白的人或物 Take the medicine.把药吃了. (2)上文提到过的人或事 He bought a house.他买了幢房子. I've been to the house.我去过那幢房子. (3)指世界上独一无二的事物 the sun ,the sky ,the moon, the earth (4)单数名词连用表示一类事物 the dollar 美元 the fox 狐狸 或与形容词或分词连用,表示一类人 the rich 富人 the living 生者 (5)用在序数词和形容词最高级,及形容词等前面 Where do you live?你住在哪? I live on the second floor.我住在二楼. That's the very thing I've been looking for.那正是我要找的东西. (6)与复数名词连用,指整个群体 They are the teachers of this school.(指全体教师) They are teachers of this school.(指部分教师) (7)表示所有,相当于物主代词,用在表示身体部位的名词前 She caught me by the arm.她抓住了我的手臂. (8)用在某些有普通名词构成的国家名称,机关团体,阶级等专有名词前 the People's Republic of China 中华人民共和国 the United States 美国 (9)用在表示乐器的名词前 She plays the piano.她会弹钢琴. (10)用在姓氏的复数名词之前,表示一家人 the Greens 格林一家人(或格林夫妇) (11)用在惯用语中 in the day, in the morning... the day before yesterday, the next morning... in the sky... in the dark... in the end... on the whole, by the way...
“tｈｅwａｙ+从句”结构的意义及用法 首先让我们来看下面这个句子: Ｒead the foｌloｗｉnｇpassagｅａnd talkａbout ｉt wi ｔh your classmaｔes．Try tｏｔeｌl whaｔyｏu thiｎk ｏf Tom aｎd ｏｆｔhe ｗay the chiｌｄrenｔrｅated ｈim. 在这个句子中，ｔhe waｙ是先行词,后面是省略了关系副词that或in whｉch的定语从句。 下面我们将叙述“the ｗaｙ+从句”结构的用法。 1.tｈe wａy之后，引导定语从句的关系词是tｈat而不是hoｗ,因此，<<现代英语惯用法词典＞＞中所给出的下面两个句子是错误的：Ｔhｉs is ｔｈeｗayｈowｉtｈａppened. This is the way how he ａlwａｙs treats me. 2．在正式语体中,thaｔ可被in which所代替;在非正式语体中，ｔhat则往往省略。由此我们得到theｗay后接定语从句时的三种模式：1) tｈe ｗaｙ＋ｔhａt-从句２）the way +ｉn whiｃh－从句3) the ｗay +从句 例如：Ｔｈe way(in whiｃh ，thａt) ｔhｅｓｅｃomｒade ｓloｏｋａｔｐrobｌems is wrong．这些同志看问题的方法
不对。 Ｔｈｅｗａy(that ,ｉn ｗhiｃh)yoｕ’ｒe doinｇit is comple ｔeｌy crａzy.你这么个干法,简直发疯。 Wｅａdmiｒｅd him for thｅｗay ｉｎｗhｉch he fａｃeｓdiｆficultieｓ． Waｌlａｃe ａｎd Ｄarwiｎｇreed ｏn the way ｉｎwhi ｃh ｄｉfｆｅrｅnt forms ｏf life had ｂegｕn.华莱士和达尔文对不同类型的生物是如何起源的持相同的观点。 Thｉs is ｔｈe ｗaｙ（tｈａｔ) hｅdｉd it. I lｉkeｄｔhe waｙ(ｔhat) ｓｈeｏｒganized ｔhe ｍeetｉng． 3.theｗay(tｈat)有时可以与hｏw(作“如何”解）通用。例如： That’s tｈe ｗaｙ(tｈaｔ) shｅspoke. = Tｈat’s how shｅspoke.
知己的近义词反义词及知己的造句 本文是关于知己的近义词反义词及知己的造句，感谢您的阅读！ 知己的近义词反义词及知己的造句知己 基本解释：顾名思义是了解、理解、赏识自己的人，如"知己知彼，百战不殆";更常指懂你自己的挚友或密友，它是一生难求的朋友，友情的最高境界。正所谓:"士为知己者死"。 1.谓了解、理解、赏识、懂自己。 2.彼此相知而情谊深切的人。 【知己近义词】 亲信，好友，密友，心腹，挚友，深交，相知，知交，知友，知心，知音，石友，老友，至友 【知己反义词】 仇人敌人陌路 【知己造句】 1、我们想要被人爱、想拥有知己、想历经欢乐、想要安全感。 2、朋友本应是我们的亲密知己和支持者，但对于大多数人来说，有一些朋友比起帮助我们，更多的却是阻碍。 3、那么，为什么你就认为，随着年龄的增长，比起女人来男人们的知己和丰富的人际关系更少，因此一般容易更孤独呢？ 4、他成了我的朋友、我的知己、我的顾问。 5、无论在我当州长还是总统的时候，布鲁斯都是我的密友、顾问和知己。他这样的朋友人人需要，也是所有总统必须拥有的。
6、波兰斯基有着一段声名卓著的电影生涯，也是几乎所有电影界重要人物们的挚友和同事，他们是知己，是亲密的伙伴。 7、搜索引擎变成了可以帮追我们的忏悔室，知己，信得过的朋友。 8、这样看来，奥巴马国家安全团队中最具影响力的当属盖茨了――但他却是共和党人，他不会就五角大楼以外问题发表看法或成为总统知己。 9、我们的关系在二十年前就已经和平的结束了，但在网上，我又一次成为了他精神层面上的评论家，拉拉队，以及红颜知己。 10、这位“知己”，作为拍摄者，站在距离电视屏幕几英尺的地方对比着自己年轻版的形象。 11、父亲与儿子相互被形容为对方的政治扩音筒、知己和后援。 12、这对夫妻几乎没有什么至交或知己依然在世，而他们在后纳粹时期的德国也不可能会说出实话的。 13、她把我当作知己，于是，我便将她和情人之间的争吵了解得一清二楚。 14、有一种友谊不低于爱情；关系不属于暖昧；倾诉一直推心置腹；结局总是难成眷属；这就是知己！ 15、把你的治疗师当做是可以分享一切心事的知己。 16、莉莉安对我敞开心胸，我成了她的知己。 17、据盖洛普民意调查显示，在那些自我认同的保守党人中，尽管布什仍维持72%支持率，但他在共和党领导层中似乎很少有几位知
表示“方式”、“方法”，注意以下用法： 1.表示用某种方法或按某种方式，通常用介词in(此介词有时可省略)。如： Do it (in) your own way. 按你自己的方法做吧。 Please do not talk (in) that way. 请不要那样说。 2.表示做某事的方式或方法，其后可接不定式或of doing sth。 如： It’s the best way of studying [to study] English. 这是学习英语的最好方法。 There are different ways to do [of doing] it. 做这事有不同的办法。 3.其后通常可直接跟一个定语从句(不用任何引导词)，也可跟由that 或in which 引导的定语从句，但是其后的从句不能由how 来引导。如： 我不喜欢他说话的态度。 正：I don’t like the way he spoke. 正：I don’t like the way that he spoke. 正：I don’t like the way in which he spoke. 误：I don’t like the way how he spoke. 4.注意以下各句the way 的用法： That’s the way (=how) he spoke. 那就是他说话的方式。 Nobody else loves you the way(=as) I do. 没有人像我这样爱你。 The way (=According as) you are studying now, you won’tmake much progress. 根据你现在学习情况来看，你不会有多大的进步。 2007年陕西省高考英语中有这样一道单项填空题： ——I think he is taking an active part insocial work. ——I agree with you_____. A、in a way B、on the way C、by the way D、in the way 此题答案选A。要想弄清为什么选A，而不选其他几项，则要弄清选项中含way的四个短语的不同意义和用法，下面我们就对此作一归纳和小结。 一、in a way的用法 表示：在一定程度上，从某方面说。如： In a way he was right.在某种程度上他是对的。注：in a way也可说成in one way。 二、on the way的用法 1、表示：即将来(去)，就要来(去)。如： Spring is on the way.春天快到了。 I'd better be on my way soon.我最好还是快点儿走。 Radio forecasts said a sixth-grade wind was on the way.无线电预报说将有六级大风。 2、表示：在路上，在行进中。如： He stopped for breakfast on the way.他中途停下吃早点。 We had some good laughs on the way.我们在路上好好笑了一阵子。 3、表示：(婴儿)尚未出生。如： She has two children with another one on the way.她有两个孩子，现在还怀着一个。 She's got five children，and another one is on the way.她已经有5个孩子了，另一个又快生了。 三、by the way的用法
仿照的近义词反义词和造句 仿照的近义词 【仿制解释】：仿造：～品 【模仿解释】：个体自觉或不自觉地重复他人的行为的过程。是社会学习的重要形式之一。尤其在儿童方面，儿童的动作、语言、技能以及行为习惯、品质等的形成和发展都离不开模仿。可分为无意识模仿和有意识模仿、外部模仿和内部模仿等多种类型。 仿照的反义词 【独创解释】：独特的创造：～精神ㄧ～一格。 仿照造句 一、老师让我们仿照黑板上的图画一幅画。 二、仿照下面的句式，以“只有”开头，写一结构与之相似的复句。 三、仿照例句的句子，在下面两句的横线上补写相应的内容。 四、仿照例句，以“记忆”或“友情”开头，另写一句话。 五、仿照下面两个例句，用恰当的词语完成句子，要求前后语意关联。 六、仿照开头两句句式，通过联想，在后面两句横线上填上相应的词语。 七、仿照所给例句，用下面的词展开联想，给它一个精彩的解释。 八、仿照例句，以“你”开头，另写一个句子。 九、仿照下列句式，续写两个句子，使之与前文组成意义相关的.句子。 十、我们也仿照八股文章的笔法来一个“八股”,以毒攻毒,就叫做八大罪状吧。
十一、仿照例句，任选一种事物，写一个句子。 十二、仿照下面一句话的句式和修辞，以“时间”开头，接着写一个句子。 十三、仿照例句，以“热爱”开头，另写一句子。 十四、仿照下面的比喻形式，另写一组句子。要求选择新的本体和喻体，意思完整。 十五、根据语镜，仿照划线句子，接写两句，构成语意连贯的一段话。 十六、仿照下面句式，续写两个句式相同的比喻句。 十七、自选话题，仿照下面句子的形式和修辞，写一组排比句。 十八、仿照下面一句话的句式，仍以“人生”开头，接着写一句话。 十九、仿照例句的格式和修辞特点续写两个句子，使之与例句构成一组排比句。 二十、仿照例句，另写一个句子，要求能恰当地表达自己的愿望。 二十一、仿照下面一句话的句式，接着写一句话，使之与前面的内容、句式相对应，修辞方法相同。 二十二、仿照下面一句话的句式和修辞，以“思考”开头，接着写一个句子。 二十三、仿照下面例句，从ABCD四个英文字母中选取一个，以”青春”为话题，展开想象和联想，写一段运用了比喻修辞格、意蕴丰富的话，要求不少于30字。 二十四、仿照下面例句，另写一个句子。 二十五、仿照例句，另写一个句子。 二十六、下面是毕业前夕的班会上，数学老师为同学们写的一句赠言，请你仿照它的特点，以语文老师的身份为同学们也写一句。
The way的用法及其含义（一） 有这样一个句子：In 1770 the room was completed the way she wanted. 1770年，这间琥珀屋按照她的要求完成了。 the way在句中的语法作用是什么？其意义如何？在阅读时，学生经常会碰到一些含有the way 的句子，如：No one knows the way he invented the machine. He did not do the experiment the way his teacher told him.等等。他们对the way 的用法和含义比较模糊。在这几个句子中，the way之后的部分都是定语从句。第一句的意思是，“没人知道他是怎样发明这台机器的。”the way的意思相当于how；第二句的意思是，“他没有按照老师说的那样做实验。”the way 的意思相当于as。在In 1770 the room was completed the way she wanted.这句话中，the way也是as的含义。随着现代英语的发展，the way的用法已越来越普遍了。下面，我们从the way的语法作用和意义等方面做一考查和分析： 一、the way作先行词，后接定语从句 以下3种表达都是正确的。例如：“我喜欢她笑的样子。” 1. the way+ in which +从句 I like the way in which she smiles. 2. the way+ that +从句 I like the way that she smiles. 3. the way + 从句(省略了in which或that) I like the way she smiles. 又如：“火灾如何发生的，有好几种说法。” 1. There were several theories about the way in which the fire started. 2. There were several theories about the way that the fire started.
暗示的近义词和反义词[暗示的近义词反义词和造句] 【暗示解释】：用含蓄、间接的方式使他人的心理、行为受到影响。下面小编就给大家整理暗示的近义词，反义词和造句，供大家学习参考。 暗示近义词 默示 示意 暗指 暗意暗示反义词 明说 表明 明言暗示造句 1. 他的顶级助手已经暗示那可能就在不久之后，但是避免设定具体的日期。 2. 一些观察家甚至暗示他可能会被送到了古拉格。 3. 要有积极的心理暗示，达成目标的好处不够充分，画面不够鲜明那它对你的吸引力就不够强烈，你就不易坚持下去! 4. 不要经常去试探男人，更不要以分手做为威胁，当你经常给他这种心理暗示，他的潜意识就会做好分手的打算。 5. 向读者暗示永远不必为主角担心，他们逢凶化吉，永远会吉人天相。 6. 约她一起运动，不要一下跳跃到游泳，可以从羽毛球网球开始，展示你的体魄和运动细胞，流汗的男人毫无疑问是最性感的，有意无意的裸露与靠近，向她暗示你的运动能力。 7. 正如在上一个示例所暗示的，只有在这些对象引用内存中同一个对象时，它们才是相同的。 8. 渥太华此时打出中国牌，巧妙地对美国这种行为作出警告，暗示美国如果长期这样下去的话，美国将自食其果。 9. 团长用震撼人心的嗓音喊道，这声音对他暗示欢乐，对兵团暗示森严，对前来校阅阅兵的首长暗示迎迓之意。 10. 渥太华此时打出中国牌，巧妙地对美国这种行为作出警告，暗示美国如果长期这样下去的话，美国将自食其恶果。 11. 我们需要邀请用户，为他们描述服务产品有多少好，给他们解释为什么他们需要填那些表单并且暗示他们会因此得到利益的回报。 12. 她对暗示她在说谎的言论嗤之以鼻。 14. 在力士参孙中，整首诗都强烈暗示着弥尔顿渴望他自己也能像参孙一样，以生命为代价，与敌人同归于尽。 15. 戴维既然问将军是否看见天花板上的钉子，这就暗示着他自己已看见了，当将军做了肯定的答复后，他又说自己看不见，这显然是自相矛盾。 16. 自我暗示在我们的生活中扮演着重要角色。如果我们不加以觉察，通常它会与我们作对。相反地，如果你运用它，它的力量就可以任你使唤。 17. 纳什建议太阳招兵买马，暗示去留取决阵容实力。 18. 同时，还暗示了菊既不同流俗，就只能在此清幽高洁而又迷漾暗淡之境中任芳姿憔悴。 19. 学习哲学的最佳途径就是将它当成一个侦探故事来处理：跟踪它的每一点蛛丝马迹每一条线索与暗示，以便查出谁是真凶，谁是英雄。 20. 不要经常去试探你的伴侣，更不要以分手做为威胁，试探本身就是一种不信任，当
放弃的近义词反义词和造句 下面就给大家整理放弃的近义词，反义词和造句，供大家学习参考。 放弃的近义词【废弃解释】：抛弃不用：把～的土地变成良田ㄧ旧的规章制度要一概～。 【丢弃解释】：扔掉;抛弃：虽是旧衣服，他也舍不得～。 放弃的反义词【保存解释】：使事物、性质、意义、作风等继续存在，不受损失或不发生变化：～古迹ㄧ～实力ㄧ～自己，消灭敌人。 放弃造句(1) 运动员要一分一分地拼，不能放弃任何一次取胜的机会。 (2) 我们不要放弃每一个成功的机会。 (3) 敌军招架不住，只好放弃阵地，狼狈而逃。 (4) 为了农村的教育事业，姐姐主动放弃了调回城市的机会。 (5) 都快爬到山顶了，你却要放弃，岂不功亏一篑?(6) 纵使遇到再大的困难，我们也要勇往直前，不轻言放弃。 (7) 逆境中的他始终没有放弃努力,仍满怀信心,期待着峰回路转的那一天。 (8) 听了同学的规劝,他如梦初醒,放弃了离家出走的想法。 (9) 因寡不敌众,我军放弃了阵地。 (10) 要日本帝国主义放弃侵华野心,无异于与虎谋皮。 (11) 永不言弃固然好，但有时放弃却也很美。
(12) 他这种放弃原则、瓦鸡陶犬的行径已经被揭露出来了。 (13) 适当放弃，做出斩钉截铁的决定，才能成为人生的赢家。 (14) 他委曲求全地放弃自己的主张，采纳了对方的意见。 (17) 我们要有愚公移山一样的斗志，坚持不懈，永远不放弃，去登上梦想的彼岸!(18) 只要有希望，就不能放弃。 (19) 为了大局着想，你应该委曲求全地放弃自己的看法。 (20) 既然考试迫在眉睫，我不得不放弃做运动。 (21) 即使没有人相信你，也不要放弃希望。 (22) 无论通往成功的路途有多艰辛，我都不会放弃。 (23) 在困难面前，你是选择坚持，还是选择放弃?(24) 无论前路多么的漫长，过程多么的艰辛，我都不会放弃并坚定地走下去。 (25) 你不要因为这点小事就英雄气短,放弃出国深造的机会。 (26) 像他这样野心勃勃的政客，怎么可能放弃追求权力呢?(27) 鲁迅有感于中国人民愚昧和麻木,很需要做发聋振聩的启蒙工作,于是他放弃学医,改用笔来战斗。 (28) 我们对真理的追求应该坚持不懈，锲而不舍，绝不能随便放弃自己的理想。 (29) 感情之事不比其他，像你这样期盼东食西宿，几个男友都捨不得放弃，最后必定落得一场空。 (30) 爷爷临终前的话刻骨铭心，一直激励着我努力学习，无论是遇到多大的困难险阻，我都不曾放弃。
way 的用法 【语境展示】 1. Now I’ll show you how to do the experiment in a different way. 下面我来演示如何用一种不同的方法做这个实验。 2. The teacher had a strange way to make his classes lively and interesting. 这位老师有种奇怪的办法让他的课生动有趣。 3. Can you tell me the best way of working out this problem? 你能告诉我算出这道题的最好方法吗？ 4. I don’t know the way (that / in which) he helped her out. 我不知道他用什么方法帮助她摆脱困境的。 5. The way (that / which) he talked about to solve the problem was difficult to understand. 他所谈到的解决这个问题的方法难以理解。 6. I don’t like the way that / which is being widely used for saving water. 我不喜欢这种正在被广泛使用的节水方法。 7. They did not do it the way we do now. 他们以前的做法和我们现在不一样。 【归纳总结】 ●way作“方法，方式”讲时，如表示“以……方式”，前面常加介词in。如例1； ●way作“方法，方式”讲时，其后可接不定式to do sth.，也可接of doing sth. 作定语，表示做某事的方法。如例2，例3；
体面的近义词|反义词及造句 我们还必须迅速采取行动，为实现社会包容和人人体面工作营造有利的环境。下面是小编精选整理的体面的近义词|反义词及造句，供您参考，欢迎大家阅读。 体面的近义词： 面子、合适、美观、颜面、场合、场面、排场、得体、好看、局面 体面的反义词： 难听、寒碜、邋遢、寒酸、难看 体面造句 1、我认为，帖在墙面上的证书，并不能使你成为一个体面的人。 2、他是那里唯一的看上去很体面的人;我认为他从来没有这样好看过。 3、所有的美国人都是好的，体面的人们每天都在践行着他们的责任。 4、美国人民慷慨、强大、体面，这并非因为我们信任我们自己，而是因为我们拥有超越我们自己的信念。 5、工人就是工人，无论他们来自哪里，他们都应该受到尊重和尊敬，至少因为他们从事正当的工作而获得体面的工资。 6、然而反之有些孩子可能就是多少有些体面的父母的不良种子这一概念却令人难以接受。
7、如果奥巴马能够成就此功，并且帮助一个体面的伊拉克落稳脚跟，奥巴马和民主党不仅是结束了伊拉克战争，而是积极从战争中挽救。 8、而且，等到年纪大了退休时，他们希望能得到尊重和体面的对待。 9、爸爸，您倒对这件事处理得很体面，而我想那可能是我一生中最糟糕的一个夜晚吧。 10、有一些积极的东西，低于预期的就业损失索赔和零售销售是体面的。 11、如果你努力工作，你就能有获得一份终生工作的机会，有着体面的薪水，良好的福利，偶尔还能得到晋升。 12、体面的和生产性的工作是消除贫困和建立自给自足最有效的方法之一。 13、同时，他是一个仁慈、温和、体面的人，一个充满爱的丈夫和父亲，一个忠实的朋友。 14、几周前我们刚讨论过平板电脑是如何作为一个体面且多产的电子设备，即使它没有完整的键盘，在进行输入时会稍慢些。 15、什么才是生活体面的标准? 16、我们还必须迅速采取行动，为实现社会包容和人人体面工作营造有利的环境。 17、她告诉我人们都担心是不是必须把孩子送到国外去学习，才能保证孩子们长大后至少能过上体面正派的生活。
t h e-w a y-的用法
The way 的用法 "the way+从句"结构在英语教科书中出现的频率较高, the way 是先行词, 其后是定语从句.它有三种表达形式:1) the way+that 2)the way+ in which 3)the way + 从句(省略了that或in which),在通常情况下, 用in which 引导的定语从句最为正式,用that的次之,而省略了关系代词that 或 in which 的, 反而显得更自然,最为常用.如下面三句话所示,其意义相同. I like the way in which he talks. I like the way that he talks. I like the way he talks. 一.在当代美国英语中,the way用作为副词的对格,"the way+从句"实际上相当于一个状语从句来修饰全句. the way=as 1)I'm talking to you just the way I'd talk to a boy of my own. 我和你说话就象和自己孩子说话一样. 2)He did not do it the way his friend did. 他没有象他朋友那样去做此事. 3)Most fruits are naturally sweet and we can eat them just the way they are ----all we have to do is clean or peel them . 大部分水果天然甜润,可以直接食用,我们只需要把他们清洗一下或去皮.
近义词反义词大全有关聆听的反义词近义词和造句 【聆听解释】：1.汉扬雄《法言.五百》:"聆听前世﹐清视在下﹐鉴莫近于斯矣。"后多用于书面语﹐常指仔细注意地听。下面就给大家聆听的反义词，近义词和造句，供大家学习参考。 嘱咐 [注释]1.叮嘱，吩咐 倾听 [注释]1.侧着头听。 2.细听;认真地听 凝听 [注释]1.聚精会神地听 谛听 [注释]注意地听;仔细听：侧耳谛听 细听 [注释]… 1. 学校应充分利用社会的各种，调动一切可以调动的力量，应尽一切可能请进名师专家，让教师和学生有较多的机会聆听大师的声音、与大师对话。这多少会机器他们向往大师、成为大师的冲动，多少会使他们觉得大师就在身边，大师并不遥远。 2. 今天妈妈带我来到了这个美丽的大安森林公园玩，这儿有漂亮的花儿和绿油油的树，草，看起来像一个我好想好想要的花园。树
上有小鸟儿在唱歌，有蝉声在帮忙和弦，树下有小朋友再安静仔细聆听著这最自然的交响曲。 3. 静静聆听那滴滴答答的声音，不知在为谁倾诉?她在偶尔低语，偶尔高唱，偶尔呻吟，偶尔叹息，生动灵活，充满了立体与层次之美，在心灵中轻轻一吻，便触发了思绪，这思绪时而短暂，时而绵长，时而深沉，时而悠远…… 4. 九月的手掌拂去小溪夏日的狂躁，用心聆听着秋日的私语，温顺地弹唱着九月醉人的秋歌，惹得天空湛蓝高远，碧空如洗。 5. 我光着脚丫，踩着海水，注视着波光粼粼的海面，听着哗哗的响声，那声音好像高超演奏家的激越的钢琴曲，又像歌唱家的雄浑的进行曲，那声音使胸膛激荡，热血奔涌，啊，我多么愿意聆听大海老人的谆谆教诲啊! 6. 雪花飘飘，寒风阵阵，我细细地聆听着寒风谱写成的有声有色的乐谱，为雪花的舞步增添一些光彩。寒风真像一位默默为雪花服务的人。过了一会儿，又为雪花写了一篇朗朗上口的诗歌，一会儿又为她热烈地鼓起了清脆的掌声。
way的用法总结大全 way的用法你知道多少，今天给大家带来way的用法，希望能够帮助到大家，下面就和大家分享，来欣赏一下吧。 way的用法总结大全 way的意思 n. 道路,方法,方向,某方面 adv. 远远地，大大地 way用法 way可以用作名词 way的基本意思是“路,道,街,径”,一般用来指具体的“路,道路”,也可指通向某地的“方向”“路线”或做某事所采用的手段,即“方式,方法”。way还可指“习俗,作风”“距离”“附近,周围”“某方面”等。 way作“方法,方式,手段”解时,前面常加介词in。如果way前有this, that等限定词,介词可省略,但如果放在句首,介词则不可省略。
way作“方式,方法”解时,其后可接of v -ing或to- v 作定语,也可接定语从句,引导从句的关系代词或关系副词常可省略。 way用作名词的用法例句 I am on my way to the grocery store.我正在去杂货店的路上。 We lost the way in the dark.我们在黑夜中迷路了。 He asked me the way to London.他问我去伦敦的路。 way可以用作副词 way用作副词时意思是“远远地,大大地”,通常指在程度或距离上有一定的差距。 way back表示“很久以前”。 way用作副词的用法例句 It seems like Im always way too busy with work.我工作总是太忙了。 His ideas were way ahead of his time.他的思想远远超越了他那个时代。 She finished the race way ahead of the other runners.她第一个跑到终点,远远领先于其他选手。 way用法例句