基于词模式嵌入的词语上下位关系分类

提出一种基于词模式的上下位关系分类方法, 可以有效地缓解传统的基于模式的分类方法存在的稀疏问题, 提高了关系分类的召回率。进一步地, 通过词模式嵌入, 将基于模式的方法与基于词嵌入的方法进行有效的融合。为了验证方法的有效性, 标注一个包含12000个汉语词语对的数据集。实验结果表明, 该词模式嵌入方法是有效的, F1值可以达到95.36%。

Hypernym Relation Classification Based on Word Pattern

The authors propose a hypernym relation classification method based on word pattern, which can effectively alleviate the sparsity problem suffered by the traditional path-based method. Furthermore, this paper makes an effective combination of the path-based method and the distributional method via word pattern embedding. To demonstrate the effectiveness of the proposed approach, the authors manually annotated a Chinese hypernym dataset containing 12000 word pairs. The experimental results show that the proposed word pattern embedding approach is effective and can achieve an F1 score of 95.36%.