randomness什么是termpaper包括哪些内容

以 my wonderful memory 为题写英语作文

my wonderful memory My best memory is from childhood.We used to live in a trailer (haha) and it was at the bottom of a hill.I was probably about five and my brother was three (the littlest was not born yet).Whenever it would rain,our yard would lit粻缉纲垦蕺旧告驯梗沫erally fill up like a pool.My mom would put us in our trashiest clothes and call the neighbor kids (who we were all very close to) and we would set up the slide and play in the rain for as long as we could without getting cold.It was an amazing time and I remember it so fondly.My mom was right out there with us.She was a great,involved mother.But just the randomness of being allowed to play in the rain with my friends was so awesome.

entropy的英文定义

entropy [简明英汉词典] n.[物]熵, [无]平均信息量 【复数】 en.tro.pies Symbol S For a closed thermodynamic system, a quantitative measure of the amount of thermal energy not available to do work. 符号 S 熵：在封闭的热力体系中不能做功的一定数量的热能的计量单位 A measure of the disorder or randomness in a closed system. 随机计量单位：在封闭体系中对无序和随机的计量单位 A measure of the loss of information in a transmitted message. 转送信息缺损值：在转送信息中的信息缺损值 A hypothetical tendency for all matter and energy in the universe to evolve toward a state of inert uniformity. 退降：宇宙中物质和能量降值惰性均匀状态的一种假设性趋势 Inevitable and steady deterioration of a system or society. 恶化，败坏：系统或社会不可避免的无法逆转的恶化或败坏

墒 是什么意思

墒〈名〉 1、耕地时开出的垄沟 2、田地里土壤的湿度 例：墒沟、墒情、墒土 墒shāng土壤里的湿度：～情。

保～。

验～。

够～。

负墒是什么意思

首先需要【明确】一下，是否是问【负熵】？ 基本释义　　 熵 shang 【拼音】：[shāng] 详细释义　　 1：物理学上指热能除以温度所得的商，标志热量转化为功的程度。

2: 科学技术上用来描述、表征系统不确定程度的函数。

亦被社会科学用以借喻人类社会某些状态的程度。

3：传播学中表示一种情境的不确定性和无组织性。

英文释义：The degree of randomness or disorder in a thermodynamic system. 在物理学中，【熵】的方向，总是增加的，这方面称为【正熵】，尽管平常不这么叫的。

后来人们引伸了这方面的含义，将能够降低【熵】数值的因素，称为【负熵】。

研究表明，【负熵】是与生命、有序的结构等有关的 负熵（墒）是什么意思==============＞能够降低【熵】数值的因素，称为【负熵】 如果觉得满意的话，请选一下那个【满意】哦。

谢谢……

什么是termpaper包括哪些内容

termpape就是“学期报告” 示例： Termpaper for the Course of Translation Computer Translation of Homonymy and Polysemy In this paper, I want to puter translation from the perspective of its treatment to homonymy and polysemy. The term homonymy is used when one form has two or more unrelated meanings. Examples of homonyms are the pair bank (of a river)—bank (financial institution). The term polysemy refers to a word with related meanings. For instance, the word “head” can be used to describe the object on of the body, that of a glass of beer, or the chief of a department of pany. First, I will explain the importance of the treatment to homonymy and polysemy in translation from Sociosemiotic theories. The study of meaning plays an essential role in the school of Sociosemiotics. Meaning is defined from the angle of the relationship between the sign and something out it, in other words, the attribute of the sign or the symbol. Moreover, the relationship is classified into that between signs and entities, between signs and users, and between signs themselves, giving rise to three kinds of meanings: referential meaning, pragmatic meaning and intralingual meaning. Referential meaning serves as the foundation of translation. Without correct identification of referential meaning, translation will either be built on sand or turn into the castle in the air. The school of Sociosemiotics attaches great importance to the realization of meaning potentials. The correct reference is a must for this process of realization. Almost every noun has its referential meaning, but as a special group of nouns, homonymy and polysemy pose special difficulty in finding out their referential meanings. As explained at the beginning, homonymy and polysemy both have a number of meanings, related or unrelated. Therefore in translation, the translator is faced with the immediate problem of identifying the correct reference within the context. In other words, he has to carry out the job of disambiguation. This is the same puter translation. Sadly, important as the problem may be, it seems to be overlooked. The testing guideline for machine translation, detailed enough to include a classification of the plural forms of nouns, excludes the test of polysemy and homonymy. It is of great importance for us to examine puters deal with this problem, no matter how difficult it may prove to be. In order to have a clear idea of the difficulty puter in handling this problem, we need to first look into the process of how human beings resolve this problem. Human beings can clear away this kind of ambiguity by resorting to his cognitive abilities. Theoretically, the process of disambiguation goes through the following three steps. I will take “bank” as an example to illustrate how the hearer can clarify the ambiguity due to its feature of homonymy. Suppose two friends run into each other near a river. One says, “the bank is beautiful.” How does the hearer interpret the reference of “bank”? First, he resorts to the physical surrounding and the reference of riverbank es to mind. In this way, the ambiguity is removed. Suppose two friends are in a dormitory chatting. One says, “the bank is beautiful.” How can the hearer interpret this word without any hint from physical surrounding? In this case, he has to trace this word back to his working memory, and then it urs to his that they met each other on the riverbank the day before. Therefore, he realizes that the speaker must be referring to the place they met. Working memory is the second step if no reference can be drawn from physical surroundings. Suppose, two friends meet on street and one says, “I am on my way to the bank. They sent me the wrong bill again!” In this context, the hearer can no longer rely on the physical surrounding or working memory for disambiguation. Therefore, he has to employ his “knowledge of the world” for clarification, which will tell him that a riverbank can never send someone a bill. In this way, he can figure out that the speaker is talking about the financial institute. The above is an illustration put forward by Sperber and Wilson as an example of cognitive capabilities of human beings. From this example, we can see that one resorts to his physical surroundings, working memory and knowledge of the word to clarify the ambiguity caused by homonymy. Therefore, it seems puter has to be equipped with the same abilities in order to perform the job. Let’s examine the translation of one famous software “东方快车” in China. Generally speaking, the translation software can to some extent distinguish different references of a word and pick out the correct one based upon the context. The following translation is done by “东方快车2003”. 1. The bird has interesting bills. 鸟有有趣的帐单。

2. The bill was passed. 法案被通过。

3. I was sent the wrong bill by pany. 我被公司送错误的法案。

4. I received my bill yesterday. 昨天我收到帐单。

Here the homonymy “bill” is translated within different context. Though the sentences are short, they have provided enough clues for disambiguation. Most people won’t have much difficulty in pointing out the right referential meaning of the word in these sentences. However, as we can see,puters are incapable of clear identification. The first translation does not make any sense, which may be attributed to the plete collection of referential meanings of “bill”. Here the word means the jaws of a bird together with their horny covering. Probably this meaning is excluded from the dictionary. The translation of the third sentence is also problematic. Most people will interpret the bill as a kind of ount, rather than legal bill. This mistranslation should be attributed to a different reason, since the referential meaning of ount is included in the dictionary, as illustrated in the translation of the fourth sentence. Apparently here puter fails to identify the correct referential meaning. Let’s try another word “head”. The following translation is performed by the same software. 1. Use your head and think! 使用你的头并且想！ 2. Two heads are better than one. 2个头是更好与比一个。

3. Heads, I win. 头，我赢。

4. He is at the head of class. 他在班的头。

5. He is the head of school. 他是学校的头。

6. He is the head of the English Department. 他是英语的部门的领导。

This group of sentences contains a number of mistranslations as well. Like the word “bill”, the referential meanings of “head” are plete, evidenced by the translation of the third sentence. The first two sentences are not translated very well, because “head” in both these two sentences refers to something intellectual instead of something physical. What is more interesting is the last two sentences, in which the word should be interpreted as “chief of anization” both. However, with almost the same structure, the word is translated differently. From parison, it is clear that there is still great randomness in choosing the correct referential meaning. This kind of arbitrary choice can be further proved by the following example. I was sent the bill. 我被送帐单。

I was sent the wrong bill. 我被送错误的法案。

It is ridiculous to change the translation of “bill” simply because an adjective is added. More than that, this adjective makes it more explicit that the bill here refers to ount instead of legal bill. Randomness in choice of referential meaning asserts itself clearly. The reason for this kind of mistranslation is self—evident. Computers are not endowed with cognitive capabilities, at least at present, to identify the correct referential meaning, by means of physical surrounding, working memory or knowledge of the world. puters can rely on is from sheer input. Therefore, the question puter translation is how to make the full use of input. To give the question a tentative answer, I propose a solution from the theory of Relevance. As the latest theory in Pragmatics, the theory of Relevance starts from the field of conversation analysis and has been playing an increasingly important role in various linguistic areas. Its application has even been extended beyond literary works to fields like advertisement. There are two major ideas that can be borrowed from the theory puter translation. One is the principle of economy and the other is the viewpoint of reconstruction. The theory of Relevance holds the belief that hearers of a conversation always try to make the least effort prehend as much as possible, which is called the principle of economy. This principle is very enlightening in the design of translation software. For instance, when people see the word “bill” in their daily life, most people will associate it with all kind of ounts they receive every day. Therefore, when they encounter the word in natural conversation, they always presuppose the referential meaning of word to be ounts, rather than a kind of legal document, unless something else in this context changes their mind, like the phrase “Bill of Rights”. As long as the presuppositional meaning does not prehension, the hearer won’t bother to make any effort to change it. In the same way, every word or phrase should be given a “default meaning” puter translation. It is particularly important for homonymy and polysemy, since they contain two or even more referential meanings. If every homonymy and polysemy can be given a default meaning, as long as the default meaning does not hinder the translation of the rest of the sentence, there is no need to try another referential meaning. The process is the same as how human beings understand conversations ording the theory of Relevance. In this way, more resources can be saved to analyze and translate other parts of the sentence. Inevitably another question follows: human beings can set “default meaning” of a word by his life experience, how puter do so? In order to solve this problem, we need to resort to corpus technology. In Corpus Linguistics, it has e a widely—epted practice to describe a word from the resources in a certain corpus. The same can be applied to the classification of the referential meaning of homonymy and polysemy. The default meaning should be the referential meaning with the highest frequency, based upon the calculation of the relevant items collected in the corpus. The other viewpoint from the theory of Relevance that can shed some light puter translation is the reconstruction theory. It is a natural step when one’s presuppositional meaning cannot fit in the context any more. He has e up with a new referential meaning and reconstruct the context. The question puter translation is how can puter know the default meaning of a homonymy or polysemy does not fit into the linguistic context. A number of approaches can be tried, and I choose the approach from collocation, in particular prepositional collocation to illustrate the process. If a homonymy or polysemy is within a prepositional phrase, the preposition associated with the noun can help puter to decide on the correct referential meaning. Take “bank” as an example. If puter locates the preposition “in” near the noun, chances are that here the word “bank” here refers to the financial institute. However, if the preposition is “on”, in most cases the word means the bank of the river. Of course, it is only an elementary judgment. One cannot count on the preposition to make out the definite meaning. Sentences like the following one may pose considerable difficulty to puter. I will give a talk on the bank in China. Normally, syntactic analysis of translation software will treat “on the bank in China” as one prepositional phrase, instead of the modifier of the noun “talk”. However, no matter which referential meaning one chooses to interpret this sentence, the following translation done by the software makes little sense. 在银行上在中国我将给一次谈话。

The link between the preposition and the noun still needs statistical verification from corpus. Here I can only give a brief explanation to demonstrate that to some extent corpus linguistics is a viable and valuable approach to solve this problem. Though 12.21% of all words in Brown Corpus are propositions, (in LOB Corpus the corresponding figure is 12.34%) about 90% of all prepositional use is made up of only 14 most frequent prepositions. Therefore, the question left unsolved in how to enumerate the possible nouns following the prepositions. Of course, it is a fantasy to list all the possible nouns that can go after a particular preposition. Nevertheless, it is possible to exhaust all the homonymy and polysemy mon use. More importantly, if we specify the preceding prepositions before a homonymy or polysemy, the likelihood of mistranslation can be reduced to a great extent. Numerous examples can be found among the homonymy. If prepositions can be taken into ount puter translation, the following mistranslations done by “东方快车2003”can be avoided. At the point of: 在意义的 To the point of 到意义的 In the point of: 在意义的 Obviously, the respective prepositions and the noun “point” are translated separately and then added together. puter can take into consideration other hints like the articles in translating prepositional phrases with homonymy or polysemy, chances are that mistranslation can be further removed. For instance, both the prepositional phrases “at the heart of” and “at heart” are listed as frequent urring collocations in LOB Corpus. Needless to say, they differ in meaning, in spite of the identical preceding preposition. Therefore, what puter can rely on within the phrase to differentiate the meaning is no other than the existence of the article “the”. As a matter of fact, prepositional phrase is only one kind of collocation. The more collocations (or formulaic phrases) can be taken into consideration puter translation, the more urate puter translation will e. In this paper, I puter translation of homonymy and polysemy. Correct identifying of the referential meaning is a must for translation, since it is described as the basis prehension in Sociosemiotics. Homonymy and polysemy are a group of words that pose special difficulty to the identification of referential meaning, especially puter, which are not endowed with reasoning capacity like human beings. Important as the problem might be, it is overlooked and thereby leads to considerable mistranslation. I analyze the reasons for this mistranslation e up with the linguistic theory of Relevance to look at the issue. It is suggested that a default meaning of a homonymy or polysemy should be set up and reconstructing the meaning be necessary from the context. A corpus—based approach should be considered in doing so. From the analysis of the collocation of homonymy and polysemy, it is more likely for puter to figure out the correct referential meaning. Reference: Biber, D. et al. Corpus Linguistics. Foreign Language Teaching and Research Press, 2000. Halliday, M.A.K. Language as Social Semiotic: The Social Interpretation of Language and Meaning. Foreign Language Teaching and Research Press, 2001. Kennedy, G. An Introduction to Corpus Linguistics. Foreign Language Teaching and Research Press, 2000. Sperber, D et al. Relevance: Communication and Cognition. Foreign Language Teaching and Research Press, 2001. Thomas, J. et al. Using Corpus for Language Research. Foreign Language Teaching and Research Press, 2001. Yule, G. The Study of Language. Foreign Language Teaching and Research Press, 2000. 何自然. Grice语用学说与关联理论. 外语教学与研究，1995：23—27. 熊学亮. Cognitive Pragmatics. 上海外语教育出版社，1999.