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Register a new userA Corpus-based Syntactic Analysis of Two-termed Unlike Coordination
التحليل النحوي المستندة إلى Corpus من التنسيق على عكس المستهدفين
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التنسيق هو ظاهرة اللغة التي تصدر شرطين أو أكثر من العبارات أو العبارات باستخدام التنسيق. على الرغم من أن التنسيق قد تم استكشافه على نطاق واسع في أدب اللغويات، فإن القواعد والقيود التي تحكم هيكلها لا تزال بعيد المنال إلى حد كبير وناقشت على نطاق واسع بين اللغويين. تقدم هذه الورقة دراسة لوجود تنسيقات على عكس المدىين على وجه الخصوص، حيث يشكل الزوجان من عبارة التنسيق مكونا صالحين ولكن لديهم فئات مميزة. أجرينا تحليلا نصنيا لفئات الجمل الفعلية التي يمكن ارتباطها في مثل هذه التنسيقات على عكس ذلك من خلال نهج محاسبي قائم على الكائنات الحاسوبية، باستخدام كوربوس اللغة الإنجليزية الأمريكية المعاصرة (COCA) كمصدر بيانات رئيسي، بالإضافة إلى بنك بنسار (PTB) وبعد تظهر النتائج أن اثنين من الالتحاق داخل التنسيقات على عكس عرض خصائص مختلفة بناء على موقفها، ودعم عرض مضاد للتنسيق لهيكل التنسيق. يوفر هذا البحث بيانات ووجهات نظر جديدة من خلال استخدام التقنيات الإحصائية التي يمكن أن تساعد في تشكيل نظريات ونماذج التنسيق في المستقبل.
Coordination is a phenomenon of language that conjoins two or more terms or phrases using a coordinating conjunction. Although coordination has been explored extensively in the linguistics literature, the rules and constraints that govern its structure are still largely elusive and widely debated amongst linguists. This paper presents a study of two-termed unlike coordinations in particular, where the two conjuncts of the coordination phrase form valid constituents but have distinct categories. We conducted a syntactic analysis of the phrasal categories that can be conjoined in such unlike coordinations through a computational corpus-based approach, utilizing the Corpus of Contemporary American English (COCA) as the main data source, as well as the Penn Treebank (PTB). The results show that the two conjuncts within unlike coordinations display different properties based on their position, supporting an antisymmetric view of the structure of coordination. This research provides new data and perspectives through the use of statistical techniques that can help shape future theories and models of coordination.
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