MIT Researchers Develop 'Web-Surfing' Machine Learning System

What do you do when you're reading an article or paper, one that it's very important you understand, and get stumped by a particular passage? More often than not, you'll head over to Google--or whatever your favorite search engine is--start surfing the Web, and won't stop until you find a satisfactory answer to the puzzle. Researchers at MIT have developed a machine learning system that behaves much the same way in the course of performing information extraction, the process of creating structured data from unstructured formats such as plain text. Here are the key details from MIT's newsroom: Most machine-learning systems work by combing through training examples and looking for patterns that correspond to classifications provided by human annotators. For instance, humans might label parts of speech in a set of texts, and the machine-learning system will try to identify patterns that resolve ambiguities -- for instance, when "her" is a direct object and when it's an adjective.