AI News, Artificial-intelligence system surfs web to improve its performance

Artificial-intelligence system surfs web to improve its performance

Information extraction -- or automatically classifying data items stored as plain text -- is thus a major topic of artificial-intelligence research.

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.

'In information extraction, traditionally, in natural-language processing, you are given an article and you need to do whatever it takes to extract correctly from this article,' says Regina Barzilay, the Delta Electronics Professor of Electrical Engineering and Computer Science and senior author on the new paper.

A machine-learning system will generally assign each of its classifications a confidence score, which is a measure of the statistical likelihood that the classification is correct, given the patterns discerned in the training data.

With the researchers' new system, if the confidence score is too low, the system automatically generates a web search query designed to pull up texts likely to contain the data it's trying to extract.

The system learns how to generate search queries, gauge the likelihood that a new text is relevant to its extraction task, and determine the best strategy for fusing the results of multiple attempts at extraction.

In the first case -- the database of mass shootings -- the system was asked to extract the name of the shooter, the location of the shooting, the number of people wounded, and the number of people killed.

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