Automatic Detection of Semantic Classes of Verb-Noun Collocations

Authors

  • Olga Kolesnikova Instituto Politécnico Nacional, Escuela Superior de Cómputo
  • Alexander Gelbukh Instituto Politécnico Nacional, Centro de Investigación en Computación
  • Liliana Chanona Hernández Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica

DOI:

https://doi.org/10.13053/cys-1-1-3130

Keywords:

Verb-noun collocations, lexical functions, semantic classification, supervised machine learning

Abstract

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 <noun>’, and to establish a relation and to find a solution mean ‘to create the <noun>’. 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.

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Published

2020-03-25

Issue

Vol. 24 No. 1 (2020): 24(1)2020

Section

Articles

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