Unsupervised, Non-stop Extraction of Information from the World Wide Web

人工智能长期以来的梦想是创造一个自主学习者，通过阅读各种各样的文本不断增加其知识。迄今为止，这种知识获取的尝试很少，而且通常是小规模的。然而，网络已经使一个巨大的在线文本库易于访问。作为回应，该项目研究了一系列无监督的、领域独立的、可扩展的系统，这些系统以开放式的方式从Web学习。这样的系统不仅可以提取信息，还可以扩展其本体，纳入新的类和关系。此外，系统的学习是递归的——一旦学习了新的关系，系统就会在这些关系的基础上学习新的关系，关系之间的关系，等等。该项目调查了这一过程的自动控制，并分析了这种学习形式的力量和局限性。该项目将通过研究从文本中学习的基本问题来影响自然语言处理（NLP）和机器学习（ML）社区。此外，所开发的系统可能会导致新一代的网络搜索引擎，改善信息获取，实现广泛的社会和经济影响。

A long standing dream of Artificial Intelligence has been to create anautonomous learner that continuously increases its knowledge byreading a wide variety of texts. To date, this kind of knowledgeacquisition has been attempted only rarely, and typically at smallscale. Yet the Web has made a vast library of online text readilyaccessible. In response, this project investigates a family of unsupervised,domain-independent, scalable systems that learn from the Web in anopen-ended fashion. Such systems not only extract information butalso extend their ontology, incorporating new classes and relations.Furthermore, the systems' learning is recursive -- once new relationsare learned, the system builds on these to learn new relations,relations between relations, and so on. The project investigates theautomatic control of this process, and analyzes both the power andlimitations of this form of learning.The project will impact both the natural language processing (NLP) and machine learning (ML) communitiesby investigating fundamental issues in learning from text. Inaddition, the systems developed could lead to a new generation of Websearch engines that improve information access, achieving a broadsocietal and economic impact.