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基于词模式嵌入的词语上下位关系分类 预览

Hypernym Relation Classification Based on Word Pattern
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摘要 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%。 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%.
作者 孙佳伟 李正华 陈文亮 张民 SUN Jiawei;LI Zhenghua;CHEN Wenliang;ZHANG Min(School of Computer Science and Technology,Soochow University,Suzhou 215006)
出处 《北京大学学报:自然科学版》 CAS CSCD 北大核心 2019年第1期1-7,共7页 Acta Scientiarum Naturalium Universitatis
基金 国家自然科学基金(61876116,61673289) 江苏省高校自然科学研究重大项目(16KJA520001)资助.
关键词 上下位关系分类 词模式 词嵌入 词模式嵌入 hypernym relation classification word pattern word embedding word pattern embedding
作者简介 通信作者:李正华,E-mail:zhli13@suda.edu.cn
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