Descrição do verbo pegar para processamento automático de linguagem natural
Nenhuma Miniatura disponível
Data
2013-07-26
Autores
Cruz, Lorena Silva Teixeira da
Título da Revista
ISSN da Revista
Título de Volume
Editor
Universidade Federal do Espírito Santo
Resumo
This research aims to analyze the use of the verb take in different contexts of use of the Portuguese language and describe the different sense relations that are established in relation to other arguments, to classify its realization as a full verb, verb or support component expression fixed. The motivation for developing this research came, in principle, the perception of the multiple meanings that the same word can have in Portuguese. From this insight we chose the verb take as the object of analysis by the fact that he has this characteristic. The description is made from the analysis of 64 constructions with the verb get, taking into account the syntactic-semantic properties of the verb, using formal criteria to describe its different meanings. From the result of this analysis, ranks as the verb take full verb, verb or support element fixed expression. This research has theoretical and methodological support in Theory Lexicon-Grammar (Gross, 1975) that aims to describe the regularities and irregularities in the use of language, for Automatic Natural Language Processing (PLN). This study may facilitate the identification or recognition of standards that can be formally represented as language resources to be used in automatic natural language processing in the form of electronic dictionary. The results are presented in a table that represents the linguistic resource. This table contains the list of predicate names that form sequences with the verb catch and properties that occur in use, with each of them. The job description of linguistic constructions is crucial for the success of PLN, because only with the descriptions in morphosyntactic-semantic level and the formalization of the properties described, the computer science area can develop more efficient programs in automatic natural language processing.