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Register a new userInducing Stereotypical Character Roles from Plot Structure
حث أدوار الطابع النمطية من هيكل المؤامرة
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أدوار الأحرف النمطية المعروفة أيضا باسم Archetys أو Dramatis الشخصية - تلعب وظيفة مهمة في الروايات: إنها تسهل الاتصالات الفعالة مع حزم الخصائص والجمعيات الافتراضية وتخفيف فهم أدوار تلك الشخصيات في السرد الشامل. نقدم نهج تجميع K-Wi يعني غير المدعوم بالكامل للتعلم الأدوار النمطية التي تعطى معلومات مؤامرة هيكلة فقط. نوضح هذه التقنية في نظرية فلاديمير بروتب الهيكلية للفولكلتال الروسية (تم التقاطها في كوربوس فوندنر الموسعة، مع 46 حكايات)، والتي تبين أن نهجنا يمكن أن تحفز ستة من أصل سبعة من شخصيات الدكتور الدافعة مع تدابير F1 تصل إلى 0.70 (0.58 متوسط) مع فئة إضافية لشخصيات طفيفة. لقد استكشفنا مجموعات ميزة مختلفة وتغيرات طريقة تقييم الكتلة. تشتمل مجموعة الميزات الأفضل أداء على وظائف المؤامرة و Unigrams و TF-IDF الأوزان و Adgeddings على رؤوس سلسلة Aquerence. أدوار المذكورة في كثير من الأحيان (بطل أو شرير)، أو أنماط مؤامرة مميزة بوضوح (الأميرة) متباينة بقوة أكثر من أدوار أقل تواترا أو متميزة (المرسل، المساعد، المانح). يشير تحليل الأخطاء التفصيلي إلى أن جودة السلسلة الأساسية ووظائف المؤامرة تتكلم حاسمة لهذه المهمة. نحن نقدم جميع بياناتنا ورمزنا لاستنساخ.
Stereotypical character roles-also known as archetypes or dramatis personae-play an important function in narratives: they facilitate efficient communication with bundles of default characteristics and associations and ease understanding of those characters' roles in the overall narrative. We present a fully unsupervised k-means clustering approach for learning stereotypical roles given only structural plot information. We demonstrate the technique on Vladimir Propp's structural theory of Russian folktales (captured in the extended ProppLearner corpus, with 46 tales), showing that our approach can induce six out of seven of Propp's dramatis personae with F1 measures of up to 0.70 (0.58 average), with an additional category for minor characters. We have explored various feature sets and variations of a cluster evaluation method. The best-performing feature set comprises plot functions, unigrams, tf-idf weights, and embeddings over coreference chain heads. Roles that are mentioned more often (Hero, Villain), or have clearly distinct plot patterns (Princess) are more strongly differentiated than less frequent or distinct roles (Dispatcher, Helper, Donor). Detailed error analysis suggests that the quality of the coreference chain and plot functions annotations are critical for this task. We provide all our data and code for reproducibility.
References used
https://aclanthology.org/
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