Since the start of the year, a team of researchers at Carnegie Mellon University supported by grants from the Defense Advanced Research Projects Agency and Google, and tapping into a research supercomputing cluster provided by Yahoo has been fine-tuning a computer system that is trying to master semantics by learning more like a human. Its beating hardware heart is a sleek, silver-gray computer calculating 24 hours a day, seven days a week that resides in a basement computer center at the university, in Pittsburgh. The computer was primed by the researchers with some basic knowledge in various categories and set loose on the Web with a mission to teach itself.
For all the advances in computer science, we still dont have a computer that can learn as humans do, cumulatively, over the long term, said the teams leader, Tom M. Mitchell, a computer scientist and chairman of the machine learning department.
The Never-Ending Language Learning system, or NELL, has made an impressive showing so far. NELL scans hundreds of millions of Web pages for text patterns that it uses to learn facts, 390,000 to date, with an estimated accuracy of 87 percent. These facts are grouped into semantic categories cities, companies, sports teams, actors, universities, plants and 274 others. The category facts are things like San Francisco is a city and sunflower is a plant.
NELL also learns facts that are relations between members of two categories. For example, Peyton Manning is a football player . The Indianapolis Colts is a football team . By scanning text patterns, NELL can infer with a high probability that Peyton Manning plays for the Indianapolis Colts even if it has never read that Mr. Manning plays for the Colts. Plays for is a relation, and there are 280 kinds of relations. The number of categories and relations has more than doubled since earlier this year, and will steadily expand.
The learned facts are continuously added to NELLs growing database, which the researchers call a knowledge base. A larger pool of facts, Dr. Mitchell says, will help refine NELLs learning algorithms so that it finds facts on the Web more accurately and more efficiently over time.
NELL is one project in a widening field of research and investment aimed at enabling computers to better understand the meaning of language. Many of these efforts tap the Web as a rich trove of text to assemble structured ontologies formal descriptions of concepts and relationships to help computers mimic human understanding. The ideal has been discussed for years, and more than a decade ago Sir Tim Berners-Lee, who invented the underlying software for the World Wide Web, sketched his vision of a semantic Web.
Today, ever-faster computers, an explosion of Web data and improved software techniques are opening the door to rapid progress. Scientists at universities, government labs, Google, Microsoft, I.B.M. and elsewhere are pursuing breakthroughs, along somewhat different paths.
For example, I.B.M.s question answering machine, Watson, shows remarkable semantic understanding in fields like history, literature and sports as it plays the quiz show Jeopardy! Google Squared, a research project at the Internet search giant, demonstrates ample grasp of semantic categories as it finds and presents information from around the Web on search topics like U.S. presidents and cheeses.
Still, artificial intelligence experts agree that the Carnegie Mellon approach is innovative. Many semantic learning systems, they note, are more passive learners, largely hand-crafted by human programmers, while NELL is highly automated. Whats exciting and significant about it is the continuous learning, as if NELL is exercising curiosity on its own, with little human help, said Oren Etzioni, a computer scientist at the University of Washington, who leads a project called TextRunner, which reads the Web to extract facts.
Computers that understand language, experts say, promise a big payoff someday. The potential applications range from smarter search to virtual personal assistants that can reply to questions in specific disciplines or activities like health, education, travel and shopping.
The technology is really maturing, and will increasingly be used to gain understanding, said Alfred Spector, vice president of research for Google. Were on the verge now in this semantic world.
With NELL, the researchers built a base of knowledge, seeding each kind of category or relation with 10 to 15 examples that are true. In the category for emotions, for example: Anger is an emotion. Bliss is an emotion. And about a dozen more.
2012年中考第二轮复习资料:几种考试题型讲练
中考英语复习:“主谓一致”
初中英语语法复习大全 课件
中考英语第二轮复习(二)名词讲解
中考英语情态动词用法讲解
初中英语代词用法专项讲解
中考英语主谓一致要点例析
2009年中考英语课本复习考点归纳
中考英语语法句子种类讲解资料与练习
初三英语专项复习之介词
2010年全国中考英语有关名词、冠词试题
中考英语代词简易练习题
中考英语时态复习:一般现在时
中考英语反意疑问句对应规则二十四条
中考英语复习:八种主要时态的具体用法
大庆市林甸二中2011年中考复习之——形容词、副词复习专题课件
中考英语定语从句二(课件)
八年级英语下册同义句转换讲与练
中考英语形容词比较级和最高级复习(课件)
中考英语名词复习课件
初中英语不规则动词归类记忆表
动词不定式
中考英语简单句专项复习题
大庆市林甸二中2011年中考复习之——定语从句专题课件
中考英语语法训练题
2011年河北省中考英语现在完成时复习课件
初中英语名词、数词练习题
中考英语第二轮复习(八)被动语态及练习
中考英语复习:名词
中考英语语法复习:句子成分
不限 |
英语教案 |
英语课件 |
英语试题 |
不限 |
不限 |
上册 |
下册 |
不限 |