document: Information extraction from Wikipedia : moving down the long tail Not only is Wikipedia a comprehensive source of quality information , it has several kinds of internal structure ( e.g. , relational summaries known as infoboxes ) , which enable self-supervised information extraction . While previous efforts at extraction from Wikipedia achieve high precision and recall on well-populated classes of articles , they fail in a larger number of cases , largely because incomplete articles and infrequent use of infoboxes lead to insufficient training data . This paper presents three novel techniques for increasing recall from Wikipedia 's long tail of sparse classes : ( 1 ) shrinkage over an automatically-learned subsumption taxonomy , ( 2 ) a retraining technique for improving the training data , and ( 3 ) supplementing results by extracting from the broader Web . Our experiments compare design variations and show that , used in concert , these techniques increase recall by a factor of 1.76 to 8.71 while maintaining or increasing precision .
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