Problem Domain Ontology Mining Based on Distributional Semantics

被引：0
作者：
Kozerenko, Elena B. [1 ]
Somin, Nikolai, V [1 ]
Sinyaghina, Yulia, I [1 ]
Pozdeeva, Svetlana A. [1 ]
机构：
[1] Russian Acad Sci, Fed Res Ctr Comp Sci & Control, Moscow, Russia
来源：
2019 6TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND COMPUTATIONAL INTELLIGENCE (CSCI 2019) | 2019年
关键词：
natural language processing; distributional semantics; ontology; syntax; vector spaces; hybrid models; machine translation; intelligent systems; multilingual knowledge extraction;
D O I：
10.1109/CSCI49370.2019.00086
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The paper presents the method of creating an extended ontology as an associative portrait of a subject area and the construction of a semantic space for intelligent knowledge extracting systems development. The ideology of semantic contextual spaces is based on the Distributional hypothesis: linguistic items with similar distributions have similar meanings. The model employed uses an extended hypothesis which includes the study of similarities and differences in contexts not only for a separate lexeme, but also for arbitrary multi-word text fragments which are considered as meaningful phrases.
引用
收藏
页码：439 / 444
页数：6
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