Investigating the Correlative Verb Collocation in Persian Language based on Cognitive Semantics
الموضوعات : مجله بین المللی علوم اجتماعیNeda Haddadi 1 , Ebrahim Sheikhzadeh 2 , Seyyed Abdulmajid Tabatabai Lotfi 3
1 - Ph.D. Student of Linguistics, Qom Branch, Islamic Azad University, Qom, Iran
2 - Assistant Professor, Department of Foreign Languages, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 - Assistant Professor, Department of English, Qom Branch, Islamic Azad University, Qom, Iran
الکلمات المفتاحية: Collocation, cognitive linguistics, Form, Schema, Prominence,
ملخص المقالة :
The present article investigates the collocation of verb and noun in Persian language by reviewing different approaches to investigate collocation, from the cognitive viewpoint. On this basis, theories of Cowie (1998) and Howarth (1996) is the theoretical framework of the present research. The present research has been conducted based on data of standard Persian language (writing) and its data were selected from corpora of Intelligent Signal Processing Research Center and Dehkhoda Dictionary. The cognitive study of the data is based on the collocation of the word "zadan". For example, for the cognitive analysis of the collocation of "del be darya zadan", different readings of the words " del be darya " and "zadan" and their combination in two sensor motor and non sensor motor domains have been investigated. It is generally concluded from the present research that in Persian language, it is possible to study the motivation of the speakers to use collocation of the words with cognitive tools such as form, schema, and prominence, and give a new classification of this linguistic phenomenon. On this basis, one can propose the classification of free and open collocation based on the degree of prominence and schema. The frequency of collocation can also be predicted based on these two cognitive criteria. The present thesis has been written in five chapters as introduction, review of literature, concepts and theoretical framework, data analysis and conclusion.
