https://scholars.lib.ntu.edu.tw/handle/123456789/413085
標題: | Structural-fitting word vectors to linguistic ontology for semantic relatedness measurement | 作者: | Lee Y.-Y. Yen T.-Y. Huang H.-H. HSIN-HSI CHEN |
關鍵字: | Linguistic ontology;Retrofitting;Semantic relatedness;Structural-fitting;Word embedding | 公開日期: | 2017 | 卷: | Part F131841 | 起(迄)頁: | 2151-2154 | 來源出版物: | International Conference on Information and Knowledge Management | 摘要: | With the aid of recently proposed word embedding algorithms, the study of semantic relatedness has progressed and advanced rapidly. In this research, we propose a novel structural-fitting method that utilizes the linguistic ontology into vector space representations. The ontological information is applied in two ways. The fine2coarse approach refines the word vectors from fine-grained to coarse-grained terms1 (word types), while the coarse2fine approach refines the word vectors from coarsegrained to fine-grained terms. In the experiments, we show that our proposed methods outperform previous approaches in seven publicly available benchmark datasets. ? 2017 Association for Computing Machinery. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/413085 | ISBN: | 9781450349185 | DOI: | 10.1145/3132847.3133152 |
顯示於: | 資訊工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。