Refer-to-as Relations as Semantic Knowledge

被引:0
|
作者
Feng, Song [1 ,2 ]
Ravi, Sujith [3 ]
Kumar, Ravi [3 ]
Kuznetsova, Polina [4 ]
Liu, Wei [5 ]
Berg, Alexander C. [5 ]
Berg, Tamara L. [5 ]
Choi, Yejin [6 ]
机构
[1] IBM TJ Watson Res Ctr, Yorktown Hts, NY 10598 USA
[2] SUNY Stony Brook, Stony Brook, NY 11794 USA
[3] Google, Mountain View, CA USA
[4] SUNY Stony Brook, Comp Sci Dept, Stony Brook, NY 11794 USA
[5] Univ N Carolina, Dept Comp Sci, Chapel Hill, NC USA
[6] Univ Washington, Comp Sci & Engn, Seattle, WA 98195 USA
基金
美国国家科学基金会;
关键词
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
We study Refer-to-as relations as a new type of semantic knowledge. Compared to the much studied Is-a relation, which concerns factual taxonomic knowledge, Refer-to-as relations aim to address pragmatic semantic knowledge. For example, a "penguin" is a "bird" from a taxonomic point of view, but people rarely refer to a "penguin" as a "bird" in vernacular use. This observation closely relates to the entry-level categorization studied in Psychology. We posit that Refer-to as relations can be learned from data, and that both textual and visual information would be helpful in inferring the relations. By integrating existing lexical structure knowledge with language statistics and visual similarities, we formulate a collective inference approach to map all object names in an encyclopedia to commonly used names for each object. Our contributions include a new labeled data set, the collective inference and optimization approach, and the computed mappings and similarities.
引用
收藏
页码:2160 / 2166
页数:7
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