A framework for specifying explicit bias for revision of approximate information extraction rules

Information extraction is one of the most important techniques used in Text Mining. One of the main problems in building information extraction (IE) systems is that the knowledge elicited from domain experts tends to be only approximately correct. In addition, the knowledge acquisition phase for building IE rules usually takes a tremendous amount of time on the part of the expert and of the linguist creating the rules. We therefore need an effective means of revising our IE rules whenever we discover such an inaccuracy. The IE revision problem is how best to go about revising a deficient IE rules using information contained in examples that expose inaccuracies. The revision process is very sensitive to implicit and explicit biases encoded in the specific revision algorithm employed. In a sense, each revision algorithm must provide two forms of biases: bias as to the place of the revision and bias as to the type of the revision that should be performed. In this paper we present a frame...

• Type: Conference
• Year: 2000
• Where: KDD
• Authors: Ronen Feldman, Yair Liberzon, Binyamin Rosenfeld, Yonatan Schler, Jonathan Stoppi
• Keywords: Data Mining, IE Revision Problem, IE Rules, KDD 2000, Revision Algorithms