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
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发表时间：
2008-05
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通讯作者：
Asuka Sumida;Naoki Yoshinaga;Kentaro Torisawa
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作者：
Asuka Sumida;Naoki Yoshinaga;Kentaro Torisawa
本文对Sumida和Torisawa提出的从维基百科的层次结构中获取上下义关系的方法（Sumida and Torisawa，2008）进行了扩展。我们从维基百科的层次布局中提取上下义关系候选者（HRC），将层次布局中项目x的所有从属项目视为x× s下义词候选者，而Sumida和Torisawa（2008）仅提取项目x的直接从属项目作为x× s下义词候选者。然后，我们选择合理的上下义关系从所获得的HRCs通过运行一个过滤器的基础上机器学习与新的功能，这甚至提高了所产生的上下义关系的精度。实验结果表明，我们获得了超过1.34万上下义关系，准确率为90.1%。
This paper proposes an extension of Sumida and Torisawas method of acquiring hyponymy relations from hierachical layouts in Wikipedia (Sumida and Torisawa, 2008). We 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 xs hyponym candidates, while Sumida and Torisawa (2008) extracted only direct subordinate items of an item x as xs hyponym candidates. We 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 we acquired more than 1.34 million hyponymy relations with a precision of 90.1%.