外刊翻译｜Sentimental journey 情感旅程
英语期刊经济学人文章翻译，觉得有用就关注我吧！
Sentimental journey
情感旅程
A new way to review scientific literature is being tested
一种正在试验中的审查科学文献的新方法
It analyses the sentiments of papers’ authors
它分析论文作者的情感
文章大概介绍：
本文介绍了一种新的分析大量文献的方法，通过分析大量保护动物话题论文中作者所表现出的情感，来观察近年来的保护濒危动物的工作是否有所作用。通过这种方法，发现论文作者的情感由消极转为积极，这说明保护动物是有成果的。不过由于保护动物本身就是一种由情感驱动的人类行为，所以这种方法行之有效，能不能运用到其他学科还有待商榷。但是已经有很多人从此分析方法中获益。
How do you measure progress? That is the question Kyle Van Houtan, an ecologist at the Monterey Bay Aquarium, in California, found himself asking when he faced the task of working out whether methods of boosting the populations of endangered species in the wild have improved over the years.
如何衡量进步？这是加州蒙特利湾水族馆的生态学家凯尔·范·霍坦在面对增加野生濒危物种数量的方法多年来是否有所改善的任务时，问自己的一个问题。
In normal circumstances, those keen on studying the effectiveness of research write reviews of the scientific literature. In a flourishing field, though, this may involve reading and extracting information from hundreds, possibly thousands, of papers. That requires a large team, and brings problems of co-ordination. Dr Van Houtan therefore wondered whether getting computers to do the heavy lifting might help.
通常情况下，那些热衷于研究效率的人会对科学文献发表见解。然而，在一个蓬勃发展的领域里，这可能涉及到阅读成百上千的论文，并从中提取信息。这就需要一个大团队，但团队之间会存在协调配合的问题。范·霍坦博士因此想知道用电脑去做这些繁重的任务是否会有所帮助。
The answer is that it does. His study on the matter, published this week in Patterns, tapped into a branch of machine learning called natural-language processing. This is a way of analysing large volumes of text with minimal human supervision. He and his colleagues identified five existing natural-language-processing systems and borrowed them. They used them to search the abstracts of 4,313 papers on species-conservation projects published over the course of the past four decades. The software’s task was to look for words associated with success, such as “protect”, “support”, “help”, “benefit” and “growth”, and also words associated with failure, like “threaten”, “loss”, “kill”, “problem” and “risk”. Different words had different values attached to them, depending on how positive or negative they were felt to be by the original model-makers. The result was that each abstract could be assigned a sentiment score, averaged from the five different inputs.
答案是肯定的。他对这个问题的研究成果已经发表在本周的《模式》杂志上，涉及到机器学习的一个分支，即自然语言处理。那是一种只需要少量的人工管理就能完成大量分析文字工作的方法。他和他的同事借用了他们认可的现有五种自然语言处理系统。他们用这五种系统搜寻了过去四十年来已经发表的关于物种保护这一领域的4313篇论文的摘要。这些软件的任务是去查找关于成功的词汇，例如：保护，支持，帮助，好处，成长，也查找关于失败的词汇，例如：威胁，损失，死亡，问题和风险。不同的词汇有不同的意义，这取决于最初的模型建造者对它们的评价是积极还是消极。结论是，每篇摘要都可以被标记一个情绪评分，取自五种不同输入值的平均值。
In total, the team analysed 1,030,558 words. They found that in papers published in the 1980s, when conservation science was in its infancy, terms from the negative list were much more common than those from the positive one. During the past decade, by contrast, terms associated with success became more frequent. Average sentiment scores increased during the study period by 140%.
这个团队总共分析了1,030,558个词汇。他们发现，在物种保护科学刚出现的1980年代发表的论文中，负面列表中的词汇比正面列表的更常见。在过去这十年恰恰相反，关于成功的词汇出现地更加频繁。它的平均情绪分数在研究期间增长了140%。
That is clearly encouraging news for conservationists. It suggests that their methods are working in general, and are improving with experience. But more detailed analysis was also possible. Giant pandas, which numbered 1,864 when censused in 2014 and had their status upgraded from “endangered” to merely “vulnerable” in September, have seen the sentiment of the literature about them swing from negative to positive in a matching way. Papers on the California condor (pictured), by contrast, remain littered with negative sentiments even though its numbers have risen, according to a census in 2016, from an extinction-threatening 22 to 446. But only 276 of those birds were wild, and so the condor is still listed as “critically endangered”.
这对自然资源保护主义者来说无疑是一个鼓舞人心的消息。这表明，他们的方法总体上是有效的，并且随着经验的增加而不断改进。但是更详细的分析有不同结论也是合理的。2014年动物数量普查时，大熊猫总共只relevent有1864只，属于濒危动物，但今年九月，大熊猫仅仅被列为了弱势动物，同时，他们发现有关大熊猫论文的情绪已同步由消极转变为积极。相反的，图片上所示的加州秃鹰，在2016年动物数量普查时，数量虽由22增加到446，但作者们还是充满消极情绪。数量有增加，但是其中只有276只是野生生物，所以秃鹰还是一直被列为严重濒危动物。
Given the numbers involved, it might be argued that both of these results were predictable. They seem, nevertheless, to be evidence that the method works. And that may be relevant in the context of the team’s analysis of rat-clearance projects intended to help species which have evolved in the absence of those subsequently introduced rodents, and also projects on small islands intended to protect populations of such autochthonous species (often these projects are the same thing). Sentiment analysis sees no clear signal of success here.
考虑到设计的数字，可能会争辩说这两种结果都是可预测的。然而，这结果似乎就是这种分析方法有效的证据。这可能在灭鼠项目的团队分析里有重要意义，这些项目旨在帮助没有随后引入的啮齿动物的情况下进化的物种，以及在小岛上开展的旨在帮助此类自生物种的种群数量(通常这些项目是一回事)。情感分析认为这些项目里没有明确的成功信号。
Whether Dr Van Houtan’s method might be generalised to other fields of science is debatable. Conservation is, at bottom, an emotion-driven activity. People care about the results in a way that goes beyond professional amour propre. That researchers’ sentiments show up in their choice of words is therefore little surprise, and might well not be true elsewhere. But the fact that Dr Van Houtan has been able to use natural-language processing to expand the pool of papers which can be taken into a review from the hundreds to the thousands suggests that others might benefit from having a look at his achievement.
Van Houtan博士的方法是否可以推广到其它学科领域还有待商榷。从本质上来说，保护是一种由情感驱动的活动。人们以超越专业爱好的方式来关心结果。因此，研究人员的情感表现在他们所使用的词汇上也就不足为奇了，在其他领域可能就并非如此了。但是，Van Houtan博士已经能够使用自然语言处理技术来扩大成被成百上千的人所评论的论文库的事实，这表明其他的人可能会受益于他的这一成果。
谢谢支持！