专业八级-596及答案解析.doc

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1、专业八级-596 及答案解析(总分：78.00，做题时间：90 分钟)一、PART LISTENING COM(总题数：0，分数：0.00)二、SECTION A(总题数：1，分数：15.00)Language Comprehension, a Cognitive Element of Reading. Introduction Reading: decoding and 1 . Language comprehensionability to 2 A. different “levels“ of language 3 children“s language B. different type

2、s of language 4 e.g. talk with friends for children: 5 formal language: 6 e.g. asking a child to retell a story C. different 7 of language comprehension explicit comprehension The listener understands what is 8 9 understanding One has to consider the context, the speaker and 10 speaker and listener“

3、s 11 in communication . Demands on children A. developing an understanding of different genres, 12 , perspectives, and styles B. understanding how those elements reflect the 13 of the speaker, author, or storyteller 14 the underlying meaning of communication C. paying attention to 15 （分数：15.00）填空项 1

4、:_三、SECTION B(总题数：2，分数：10.00)（分数：5.00）A.An actress.B.A tech blogger.C.A fashion designer.D.An athlete.A.Her dedication.B.Her regular reply.C.Her appreciation.D.Sharing of her personal life.A.Because she wants to stress the importance of politeness.B.Because she wants to explain why she replies to pe

5、ople regularly in the cyber world.C.Because she wants to describe the latest technological development that inspires her next line.D.Because she wants to emphasize the differences between the real world and the cyber world.A.To share with others her life and career.B.To invite more follower and fans

6、 for her acting career.C.To develop the potential market for her products.D.To interact with the world.A.When she was a little girl who tried to resonate with her father and brother.B.When she starred several sitcoms which made her a big shot in show business.C.When she failed to find any fashionabl

7、e female fan apparel in a stadium shop.D.When she became one of the most influential people on Twitter.（分数：5.00）A.Online.B.In Ralph Lauren.C.In stadiums.D.None of the above.A.Fashionable outfits for herself.B.Great response from women customers.C.Handsome profits.D.Her popularity “being increased.A.

8、She wants to prove that she is much more than that.B.She feels that she could do nothing.C.She feels happy and content about that.D.She wants to do more acting and break away from that image.A.37 years ago.B.17 years ago.C.Last year.D.She was not in the cast.A.By the number of their followers.B.By t

9、rusted recommendations.C.By whether they are her fans.D.By whether they are techy geeks.四、PART READING COMPR(总题数：1，分数：22.00)PASSAGE ONE A perennial problem in semantics is the delineation of, its subject matter. The term meaning can be used in a variety of ways, and only some of these correspond to

10、the usual understanding of the scope of linguistic or computational semantics. We shall take the scope of semantics to be restricted to the literal interpretations of sentences in a context, ignoring phenomena like irony, metaphor, or conversational implicature. A standard assumption in computationa

11、lly oriented semantics is that knowledge of the meaning of a sentence can be equated with knowledge of its truth conditions: that is, knowledge of what the world would be like if the sentence were true. This is not the same as knowing whether a sentence is true, which is usually an empirical matter,

12、 but knowledge of truth conditions is a prerequisite for such verification to be possible. Meaning as truth conditions needs to be generalized somewhat for the case of imperatives or questions, but is a common ground among all contemporary theories, in one form or another, and has an extensive philo

13、sophical justification. A semantic description of a language is some finitely stated mechanism that allows us to say, for each sentence of the language, what its truth conditions are. Just as for grammatical description, a semantic theory will characterize complex and novel sentences on the basis of

14、 their constituents: their meanings, and the manner in which they are put together. The basic constituents will ultimately be the meanings of words and morphemes. The modes of combination of constituents are largely determined by the syntactic structure of the language. In general, to each syntactic

15、 rule combining some sequence of child constituents into a parent constituent, there will correspond some semantic operation combining the meanings of the children to produce the meaning of the parent. A corollary of knowledge of the truth conditions of a sentence is knowledge of what inferences can

16、 be legitimately drawn from it. Valid inference is traditionally within the province of logic as is truth and mathematical logic has provided the basic tools for the development of semantic theories. One particular logical system, first order predicate calculus (FOPC), has played a special role in s

17、emantics as it has in many areas of computer science and artificial intelligence. FOPC can be seen as a small model of how to develop a rigorous semantic treatment for a language, in this case an artificial one developed for the unambiguous expression of some aspects of mathematics. The set of sente

18、nces or well formed formulae of FOPC are specified by a grammar, and a rule of semantic interpretation is associated with each syntactic construct permitted by this grammar. The interpretations of constituents are given by associating them with set-theoretic constructions from a set of basic element

19、s in some universe of discourse. Thus for any of the infinitely large set of FOPC sentences we can give a precise description of its truth conditions, with respect to that universe of discourse. Furthermore, we can give a precise account of the set of valid inferences to be drawn from some sentence

20、or set of sentences, given these truth conditions, or given a set of rules of inference for the logic. Some natural language processing tasks (e.g., message routing, textual information retrieval, translation) can be carried out quite well using statistical or pattern matching techniques that do not

21、 involve semantics in the sense assumed above. However, performance on some of these tasks improves if semantic processing is involved. Some tasks, however, cannot be carried out at all without semantic processing of some form. One important example application is that of database query, of the type

22、 chosen for the Air Travel Information Service task. For example, if a user asks, “Does every flight from London to San Francisco stop over in Reykyavik?“ then the system needs to be able to deal with some simple semantic facts. Relational databases do not store propositions of the form every X has

23、property P and so a logical inference from the meaning of the sentence is required. In this case, every X has property P is equivalent to there is no X that does not have property P and a system that knows this will also therefore know that the answer to the question is no if a non-stopping flight i

24、s found and yes otherwise. PASSAGE TWO In 1977, the group Women Office Workers held a contest for secretaries, inviting them to name the “most ridiculous personal errand“ they“d ever run. As Lynn Peril tells it in “Swimming in the Steno Pool“, her light, wry history of the secretarial profession, th

25、e winner was a woman whose boss asked her to take pictures of him before, while and after he shaved off his moustache. The runner-up“s task was to pick up her boss“s wife and newborn baby from the hospital. This is the profession“s image problem: Secretaries have to either cater to their bosses in l

26、oopy ways or contend with the idea that they might. Peril, a longtime secretary herself, is frank about how women“s clerical dominance has both helped and hindered them. Her account gives secretaries their due while making clear why they posed a problem for the equal rights movement, and vice versa.

27、 In the late 19th century, when women started taking over the field, they were paid half what men were for clerical workbut twice the salary of a public-school teacher, Peril finds. It made some sense, then, when in 1923 an inventor of the typewriter was photographed for a commemorative book with an

28、 ensemble of women in Greek gowns and the proud line “EMANCIPATION“ on the facing page. The downside was that while men could treat clerical jobs as the first rung of the office management ladder, women almost never made that climb. Instead, they were supposed to settle for reflected glory. One 1960

29、s author told her readers they could “be a lawyer“s or a doctor“s or a scientist“s secretary because you once hoped to be a lawyer or a doctor or a scientist“. Peril notes exceptions. Jane J. Martin, a stenographer turned advertising whiz whose 1921 salary would have come to $300,000 today, sounds l

30、ike a prototype for the “Mad Men“ character Peggy Olson. Katharine Gibbs, a dressmaker turned stenographer, sold her jewelry to raise money, then opened a successful chain of secretarial schools. She accepted only female students, proclaiming, “A woman“s career is blocked by lack of openings, by unj

31、ust male competition, by prejudice and; not least, by inadequate salary and recognition.“ Still, as Peril writes, it“s a mistake to think of Gibbs as a protofeminist; her school turned out “perfect secretaries in white gloves and hats whose thorough knowledge of shorthand and typing was surpassed on

32、ly by their loyalty to the boss“. Feminist also isn“t quite the right word for Helen Gurley Brown. She broke the dutiful and chaste mold as she moved up the ranks to become editor of Cosmopolitan. Yet remembering her 1940s days as a secretary at a Los Angeles radio station, she fondly described the

33、“dandy game“ of scuttle, in which a group of men picked a secretary to chase and catch so they could take off her underwear. “The girls wore their prettiest panties to work,“ Brown wrote. “Alas, I was never scuttled.“ It“s a story that justifies the most tedious office training on sexual harassment.

34、 In the 1970s, second-wave feminists missed chances to appeal to the nine million women who did clerical work. Gloria Steinem apparently didn“t make their feelings her priority when, in her 1971 commencement address at Smith College, she imagined the power of an entire generation of women refusing t

35、o learn how to type. When feminists marched for equality in New York, the director of one secretaries“ group declined to participate, declaring, “We“re not exhibitionists, and we don“t carry signs.“ Another rejected the idea that secretaries needed other women to liberate them. “We“re perfectly capa

36、ble of being our own spokesmen,“ she said, adding, “The truth is, we“re not unhappy.“ This rings true. As Peril writes, “not everyone aspired to be an executive.“ At the same time, for generations contentment was the only acceptable outlook for women in the office. One guide for secretaries urged th

37、em to be “fair and sunny . no matter how you feel.“ The journalist and author Anne Kreamer wants people in the workplacemen and womento be more comfortable expressing how they feel. In “It“s Always Personal“, she asserts that as more women are elevated to positions of power, a greater range of emoti

38、ons will become acceptable at work. “Is it a real problem that while emotion underlies nearly all important work decisions, most of us most of the time pretend that it“s not so?“ She asks rhetorically. Kreamer“s book explores how to be true to your “emotional flashpointsanger, fear, anxiety, empathy

39、, happiness and crying“without sabotaging your career. Yon can let your upper lip wobble. But you shouldn“t become the office basket case. To figure out what people actually think about the expression of emotion at work, Kreamer persuaded an advertising agency to help her conduct a nationally repres

40、entative poll of 700 workers. She found some differences between men and women, especially with regard to tears. In her survey, women were much more likely to report crying at work than men. Yet crying or not crying did not relate to how much respondents liked their jobs or how high they placed in t

41、he office hierarchy. This explains how I can love my job but also announce to my boss that since I“m probably going to cry, I“ve brought Kleenex to a meeting. Kreamer is all for Kleenex. She thinks bosses should learn to take crying in stride, though she also warns that if you use tears to manipulat

42、e, your concerns “will no longer be heard“. This is all very sensible. Kreamer is less convincing, however, when she tries to lasso brain science into her discussion of gender differences. Largely relying on Louann Brizendine, a neuropsychiatrist at the University of California, San Francisco, whose

43、 work is studded with exaggerations of brain-based sex differences that fall apart upon closer examination, Kreamer claims not only that women cry more frequently, but that they are “hardwired to do so. But if this is the case, why do girls and boys cry the same amount through childhood? Kreamer doe

44、sn“t explore the possibilities. Instead, with flimsy evidence, she proposes that men “may really have a biologically easier time dealing with difficult emotional situations.“ She thinks this kind of “scientific insight“ will diminish stereotyping. But doesn“t promoting a biological explanation for g

45、ender difference, whether or not there“s solid proof for it, make the division seem more immutable than it necessarily is? Tellingly, Kreamer found no difference between men and women in a second survey she designed to measure how individual work style lines up with how people cope with stress. From

46、 about 1,200 responses, Kreamer charts four types of workplace personalities. Men and women are distributed evenly among the groups, including the “Solvers“, who are twice as likely to be top managers. In our era, both men and women have learned to type. Kreamer“s data shows they are equally capable

47、, emotionally speaking, of running the office. If I had a secretary, I“d ask him to file that. PASSAGE THREE We like to think of ourselves as rational creatures. We watch our backs, weigh the odds, pack an umbrella. But both neuroscience and social science suggest that we are more optimistic than re

48、alistic. On average, we expect things to turn out better than they wind up being. People hugely underestimate their chances of getting divorced, losing their job or being diagnosed with cancer; expect their children to be extraordinarily gifted; envision themselves achieving more than their peers; a

49、nd overestimate their likely life span. The belief that the future will be much better than the past and present is known as the optimism bias. It abides in every race, region and socioeconomic bracket. Schoolchildren playing when-I-grow-up are rampant optimists, but so are grownups: A 2005 study found that adults over 60 are just as likely to see the glass half full as young adults. You might expect optimism to erode under the tide of news about violent conflicts, high unemployment, tornadoes and floods and all the threats and failures that shape hum