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مدل سازی معنایی شعر ویلیام باتلر ییتس با شبکههای عصبی بازگشتی: کاربرد هوش مصنوعی در تحلیل و فهم نمادگرایی ادبی | ||
| پژوهشهای ادبی ـ فلسفی | ||
| دوره 3، شماره 2 - شماره پیاپی 6، اسفند 1404، صفحه 135-168 اصل مقاله (693.14 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22080/lpr.2025.29194.1104 | ||
| نویسنده | ||
| آزاده مهرپویان* | ||
| استادیار، گروه آموزشی زبان و ادبیات انگلیسی، دانشگاه ولایت، ایرانشهر، ایران | ||
| تاریخ دریافت: 18 اردیبهشت 1404، تاریخ بازنگری: 26 مهر 1404، تاریخ پذیرش: 02 آبان 1404 | ||
| چکیده | ||
| این پژوهش با هدف مدلسازی معنایی و تحلیل فلسفی نمادگرایی در شعر ویلیام باتلر ییتس، شاعر برجستۀ نمادگرا و نئوافلاطونی قرن بیستم، انجام شده است. در این مطالعه، از شبکههای عصبی بازگشتی (RNN) بهعنوان روشی نوین در پردازش زبان طبیعی برای تحلیل ساختارهای معنایی و نمادهای پیچیده در اشعار ییتس بهره گرفته شده است. دادههای پژوهش شامل گزیدهای از اشعار منتخب ییتس است که پساز پیشپردازش و برچسبگذاری معنایی، به مدل وارد وتحلیلهای سنتی آنگونه که باید به کار گرفته نشده است. یافتههای پژوهش نشان میدهد که کاربرد یادگیری عمیق در تحلیل متون ادبی، امکان بازخوانی دقیقتر و عمیقتر مفاهیم فلسفی مانند هویت، آگاهی و تضادهای وجودی در شعر ییتس را فراهم میآورد و میتواند به غنای فهم فلسفی نمادگرایی کمک کند. این رویکرد نوآورانه، علاوهبر ارائۀ چارچوبی علمی و دادهمحور، راهگشای پژوهشهای میانرشتهای در حوزۀ ادبیات و فلسفه و نشاندهندۀ ظرفیتهای بالقوۀ هوش مصنوعی در تحلیل متون ادبی-فلسفی است. | ||
| کلیدواژهها | ||
| ویلیام باتلر ییتس؛ نمادگرایی؛ فلسفه ادبیات؛ مدلسازی معنایی؛ پردازش زبان طبیعی؛ شبکه عصبی بازگشتی؛ یادگیری عمیق | ||
| عنوان مقاله [English] | ||
| Semantic Modeling of William Butler Yeats’s Poetry Using Recurrent Neural Networks: Application of Artificial Intelligence in the Philosophical Interpretation of Literary Symbolism | ||
| نویسندگان [English] | ||
| Azadeh Mehrpouyan | ||
| Assistant Professor, Department of English Language and Literature, Velayat University, Iranshahr, Iran | ||
| چکیده [English] | ||
| This study aims to philosophically reinterpret literary symbolism in the poetry of William Butler Yeats by designing and implementing an interdisciplinary framework grounded in theories of the philosophy of literature and deep learning algorithms. Yeats, as a prominent Neo-Platonic and symbolist poet of the twentieth century, constructs a complex system of multilayered symbols and cyclical structures that reflect fundamental ontological concepts, including plural identity, reflective consciousness, and existential contradictions. Focusing on the philosophical analysis of these structures, the research seeks to develop a data-driven model for uncovering nonlinear relationships between symbolic elements and philosophical concepts within poetic texts. The methodology involves selecting and preprocessing a curated corpus of Yeats’s symbolic poems, performing semantic annotation, and modeling conceptual sequences using Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) architecture and Attention mechanisms within the framework of Natural Language Processing. These models, capable of identifying intricate semantic dependencies, recurring sequences, and multi-level structures, enable a more precise and systematic analysis of philosophical symbolism. Findings indicate that integrating deep learning algorithms with theoretical foundations in the philosophy of literature not only overcomes the limitations of traditional interpretive methods but also provides a conceptual basis for redefining symbolism as an epistemological and ontological system. This data-driven approach enhances the philosophical analysis of literary texts and contributes to methodological innovation in theoretical and applied literary studies within the emerging field of digital humanities. | ||
| کلیدواژهها [English] | ||
| William Butler Yeats, Philosophical Symbolism, Philosophy of Literature, Semantic Modeling, Recurrent Neural Networks (RNN), Deep Learning, Data-Driven Analysis | ||
| مراجع | ||
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