考研英语阅读理解基本素材经济学人科技类(3)

Research just published in the Proceedings of the Royal Society suggests, however, that it may be possible for all to have prizes. Get the dose and timing right and you can have a higher crop yield and a higher weed yield at the same time—and also use less herbicide.

The research was done at Broom's Barn Research Station in Suffolk, by a team led by Mike May, the head of the station's weeds group. The team was studying GM sugar beet. This was one of the species examined in the British government's Farm-Scale Evaluations (FSEs) project, a huge, three-year-long research programme designed to assess the effects (including the environmental effects) of herbicide use on GM crops.

The results for sugar beet, which competes badly with common weed species and thus relies heavily on the application of herbicides for its success, came in for particular criticism from environmentalists when the trials concluded in 2003. They indicated that fields planted with GM beet and treated with glyphosate, the herbicide against which the modification in question protects, had fewer weeds later in the season. These produced fewer seeds and thus led to reduced food supplies for birds. Some invertebrates, particularly insects, were also adversely affected.

The Broom's Barn researchers, however, felt that this problem might be overcome by changing the way the glyphosate was applied. They tried four different treatment “regimes”, which varied the timing and method of herbicide spraying, and compared them with conventional crop management regimes such as those used in the FSEs.

The best results came from a single early-season application of glyphosate. This increased crop yields by 9% while enhancing weed-seed production up to sixteen-fold. And, as a bonus, it required 43% less herbicide than normal. Genetic modification, it seems, can be good for the environment, as well as for farmers' pockets.

Passage 6

Corpus colossal

How well does the world wide web represent human language?

LINGUISTS must often correct lay people's misunderstandings of what they do. Their job is not to be experts in “correct” grammar, ready at any moment to smack your wrist for a split infinitive. What they seek are the underlying rules of how language works in the minds and mouths of its users. In the common shorthand, linguistics is descriptive, not prescriptive. What actually sounds right and wrong to people, what they actually write and say, is the linguist's raw material.

But that raw material is surprisingly elusive. Getting people to speak naturally in a controlled study is hard. Eavesdropping is difficult, time-consuming and invasive of privacy. For these reasons, linguists often rely on a “corpus” of language, a body of recorded speech and writing, nowadays usually computerised. But traditional corpora have their disadvantages too. The British National Corpus contains 100m words, of which 10m are speech and 90m writing. But it represents only British English, and 100m words is not so many when linguists search for rare usages. Other corpora, such as the North American News Text Corpus, are bigger, but contain only formal writing and speech.

Linguists, however, are slowly coming to discover the joys of a free and searchable corpus of maybe 10 trillion words that is available to anyone with an internet connection: the world wide web. The trend, predictably enough, is prevalent on the internet itself. For example, a group of linguists write informally on a weblog called Language Log. There, they use Google to discuss the frequency of non-standard usages such as “far from” as an adverb (“He far from succeeded”), as opposed to more standard usages such as “He didn't succeed—far from it”. A search of the blog

itself shows that 354 Language Log pages use the word “Google”. The blog's authors clearly rely heavily on it.

For several reasons, though, researchers are wary about using the web in more formal research. One, as Mark Liberman, a Language Log contributor, warns colleagues, is that “there are some mean texts out there”. The web is filled with words intended to attract internet searches to gambling and pornography sites, and these can muck up linguists' results. Originally, such sites would contain these words as lists, so the makers of Google, the biggest search engine, fitted their product with a list filter that would exclude hits without a correct syntactical context. In response, as Dr Liberman notes, many offending websites have hired computational linguists to churn out syntactically correct but meaningless verbiage including common search terms. “When some sandbank over a superslots hibernates, a directness toward a progressive jackpot earns frequent flier miles” is a typical example. Such pages are not filtered by Google, and thus create noise in

research data.

There are other problems as well. Search engines, unlike the tools linguists use to analyse standard corpora, do not allow searching for a particular linguistic structure, such as “[Noun phrase] far from

[verb phrase]”. This requires indirect searching via samples like “He far from succeeded”. But Philip Resnik, of the University of Maryland, has created a “Linguist's Search Engine” (LSE) to overcome this. When trying to answer, for example, whether a certain kind of verb is generally used with a direct object, the LSE grabs a chunk of web pages (say a thousand, with perhaps a million words) that each include an example of the verb. The LSE then parses the

sample, allowing the linguist to find examples of a given structure, such as the verb without an

object. In short, the LSE allows a user to create and analyse a custom-made corpus within minutes.

The web still has its drawbacks. Most of it is in English, limiting its use for other languages (although Dr Resni …… 此处隐藏：5134字，全部文档内容请下载后查看。喜欢就下载吧 ……