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Register a new userProfiling of Intertextuality in Latin Literature Using Word Embeddings
تنميط Intertextuality في الأدب اللاتيني باستخدام Word Embeddings
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تحديد العلاقات بين المؤلفين بين المؤلفين ذات أهمية مركزية لدراسة الأدبيات. نقوم بالإبلاغ عن تحليل تجريبي بين التقاطعات التعليمية في الأدبيات اللاتينية الكلاسيكية باستخدام نماذج تضمين كلمة. لتمكين التقييم الكمي لطرق البحث Intertextuxucture، نرفع مجموعة بيانات جديدة من 945 موازية معروفة تم رسمها من المنحة التقليدية على الشعر الملحمي اللاتيني. نقوم بتدريب نموذج Word2VEC الأمثل على كائن كبير من اللاتينية Lemmatized، والذي يحقق أداء حديثة للكشف عن المرادف والتفوق بطريقة معجمية تستخدم على نطاق واسع للبحث Intertextual. ثم نوضح بعد ذلك أن تضمينات التدريب في كورسيا الصغيرة جدا يمكن أن تلتقط الجوانب البارزة للأسلوب الأدبي وتطبيق هذا النهج على تكرار دراسة Intertextual السابقة ل Livy المؤرخ الروماني، والتي اعتمدت على ميزات أنالومترية يدوية باليد. تقدم نتائجنا تطوير الموارد الحسابية الأساسية لغلق رئيسي رئيسي وتسليط الضوء على شارع إنتاجي للتعاون متعدد التخصصات بين دراسة الأدب و NLP.
Identifying intertextual relationships between authors is of central importance to the study of literature. We report an empirical analysis of intertextuality in classical Latin literature using word embedding models. To enable quantitative evaluation of intertextual search methods, we curate a new dataset of 945 known parallels drawn from traditional scholarship on Latin epic poetry. We train an optimized word2vec model on a large corpus of lemmatized Latin, which achieves state-of-the-art performance for synonym detection and outperforms a widely used lexical method for intertextual search. We then demonstrate that training embeddings on very small corpora can capture salient aspects of literary style and apply this approach to replicate a previous intertextual study of the Roman historian Livy, which relied on hand-crafted stylometric features. Our results advance the development of core computational resources for a major premodern language and highlight a productive avenue for cross-disciplinary collaboration between the study of literature and NLP.
References used
https://aclanthology.org/
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