Boosting Precision and Recall of Hyponymy Relation Acquisition from Hierarchical Layouts in Wikipedia

Boosting Precision and Recall of Hyponymy Relation Acquisition from Hierarchical Layouts in Wikipedia - scientific work related to Wikipedia quality published in 2008, written by .

Overview

This paper proposes an extension of Sumida and Torisawa’s method of acquiring hyponymy relations from hierachical layouts in Wikipedia (Sumida and Torisawa, 2008). Authors extract hyponymy relation candidates (HRCs) from the hierachical layouts in Wikipedia by regarding all subordinate items of an item x in the hierachical layouts as x’s hyponym candidates, while Sumida and Torisawa (2008) extracted only direct subordinate items of an item x as x’s hyponym candidates. Authors then select plausible hyponymy relations from the acquired HRCs by running a filter based on machine learning with novel features, which even improve the precision of the resulting hyponymy relations. Experimental results show that authors acquired more than 1.34 million hyponymy relations with a precision of 90.1%.