Automatic Detection of Semantic Classes of Verb-Noun Collocations

被引:0
|
作者
Kolesnikova, Olga [1 ]
Gelbukh, Alexander [2 ]
Chanona-Hernandez, Liliana [3 ]
机构
[1] Inst Politecn Nacl, Escuela Super Comp, Mexico City, DF, Mexico
[2] Inst Politecn Nacl, Ctr Invest Comp, Mexico City, DF, Mexico
[3] Inst Politecn Nacl, Escuela Super Ingn Mecan & Elect, Mexico City, DF, Mexico
来源
COMPUTACION Y SISTEMAS | 2020年 / 24卷 / 01期
关键词
Verb-noun collocations; lexical functions; semantic classification; supervised machine learning;
D O I
10.13053/CyS-24-1-3130
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
It does not surprise us that a bank can be a financial institution as well as a piece of land. Quite often one word is used with different meanings. But sometimes the opposite happens: we choose different words to express the same idea. For example, to give a smile means 'to smile', and to lend support means 'to support' (Longman Dictionary of Contemporary English, 1995). These two collocations convey the same idea: to smile is to 'perform', or 'do' a smile, and to support is to 'do' support, so that both verb-noun collocations share the same semantics: to do what is denoted by the noun. Likewise, we find that to acquire popularity and to sink into despair both mean 'to begin to experience the ', and to establish a relation and to find a solution mean 'to create the '. Such semantic patterns or classes are called lexical functions. In this article, we explain the concept of lexical functions, give a summary of state-of-the-art research on automatic detection of lexical functions, and present the framework and results of our experiments on supervised learning of lexical functions fulfilled on the material of Spanish verb-noun collocations.
引用
收藏
页码:141 / 150
页数:10
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