GENERATING SEMANTIC SIMILARITY ATLAS FOR NATURAL LANGUAGES

被引:0
|
作者
Senel, Lutfi Kerem [1 ,2 ,3 ]
Utlu, Ihsan [1 ,2 ]
Yucesoy, Veysel [1 ]
Koc, Aykut [1 ]
Cukur, Tolga [2 ,3 ,4 ]
机构
[1] ASELSAN Res Ctr, Ankara, Turkey
[2] Bilkent Univ, Dept Elect & Elect Engn, Ankara, Turkey
[3] Bilkent Univ, UMRAM, Sabuncu Brain Res Ctr, Ankara, Turkey
[4] Bilkent Univ, Neurosci Program, Ankara, Turkey
来源
2018 IEEE WORKSHOP ON SPOKEN LANGUAGE TECHNOLOGY (SLT 2018) | 2018年
关键词
cross-lingual semantic similarity; natural language processing; semantic similarity; word embedding; computational linguistics;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Cross-lingual studies attract a growing interest in natural language processing (NLP) research, and several studies showed that similar languages are more advantageous to work with than fundamentally different languages in transferring knowledge. Different similarity measures for the languages are proposed by researchers from different domains. However, a similarity measure focusing on semantic structures of languages can be useful for selecting pairs or groups of languages to work with, especially for the tasks requiring semantic knowledge such as sentiment analysis or word sense disambiguation. For this purpose, in this work, we leverage a recently proposed word embedding based method to generate a language similarity atlas for 76 different languages around the world. This atlas can help researchers select similar language pairs or groups in cross-lingual applications. Our findings suggest that semantic similarity between two languages is strongly correlated with the geographic proximity of the countries in which they are used.
引用
收藏
页码:795 / 799
页数:5
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