An extraction method of hyponymy based on multiple data sources fusion
Shuqi Wang, Xiao Xin Feng, Shuwu Zhang, Chengcheng Yin · 2016
Hyponymy is one of the most critical semantic relations, which contributes magnificently to semantic dictionary, information retrieval etc. In this paper, a method of extracting hyponymy is proposed based on multiple data sources fusion, which convert the extraction of hyponymy to the extraction of hypernyms for target words. First, mining candidate hypernyms for the target words based on search engine, encyclopedia resources and core suffix words. Second, fusing the candidates from the above data sources. At last, the classification algorithm is used to filter the noise and extract the hypernyms, which is a quite mature machine learning algorithm. There is hyponymy between the target words and their correctly extracted hypernyms. The experimental results show that the highest accuracy rate of hyponymy extraction reaches 0.832 using the proposed method.