Information extraction — or automatically classifying data items stored as plain text — is a major topic of artificial-intelligence research. Image: MIT News.
“Information extraction” system helps turn plain text into data for statistical analysis.
Of the vast wealth of information unlocked by the Internet, most is plain text. The data necessary to answer myriad questions — about, say, the correlations between the industrial use of certain chemicals and incidents of disease, or between patterns of news coverage and voter-poll results — may all be online. But extracting it from plain text and organizing it for quantitative analysis may be prohibitively time consuming.
Information extraction — or automatically classifying data items stored as plain text — is thus a major topic of artificial-intelligence research. Last week, at the Association for Computational Linguistics’ Conference on Empirical Methods on Natural Language Processing, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory won a best-paper award for a new approach to information extraction that turns conventional machine learning on its head.