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Register a new userFraming Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames
تأطير تفكيك: نموذج متعدد إشرافه متعدد إشرافه من إطارات الوسائط
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فهم كيفية أخبار الإطار الإعلامي القضايا السياسية مهمة بسبب تأثيرها على المواقف العامة، ولكن من الصعب أتمتة. تركزت النهج الحسابية إلى حد كبير على تصنيف إطار مقال أخبار كامل بينما غالبا ما تكون إشارات تأطير خفية ومحلية. علاوة على ذلك، فإن تحليل الأخبار التلقائي هو مجال حساس، ويفتقر الفصوص الموجودة إلى الشفافية في تنبؤاتهم. تتناول هذه الورقة كلا من المشكلات مع نموذج جديد للإشراف، والتي تتعلم بشكل مشترك تضمين المعلومات المحلية حول الأحداث والجهات الفاعلة ذات الصلة في مقالة إخبارية من خلال إطار ترميز تلقائي، والاستفادة من هذه الإشارة لتصنيف إطار الوثيقة على مستوى المستند. تظهر تجاربنا أن: تتفوق النموذج لدينا النماذج السابقة من التنبؤ الإطار؛ يمكننا زيادة تحسين الأداء مع بيانات التدريب غير المسبق التي تستفيد من الطبيعة شبه الإشرفة لنموذجنا؛ وتأثير الحدث المستفيد و Ambeddings الممثل بشكل حدسي التوقعات على مستوى الوثيقة، مما يوفر تمثيلا إطارات إطارات محمولة ومهمة.
Understanding how news media frame political issues is important due to its impact on public attitudes, yet hard to automate. Computational approaches have largely focused on classifying the frame of a full news article while framing signals are often subtle and local. Furthermore, automatic news analysis is a sensitive domain, and existing classifiers lack transparency in their predictions. This paper addresses both issues with a novel semi-supervised model, which jointly learns to embed local information about the events and related actors in a news article through an auto-encoding framework, and to leverage this signal for document-level frame classification. Our experiments show that: our model outperforms previous models of frame prediction; we can further improve performance with unlabeled training data leveraging the semi-supervised nature of our model; and the learnt event and actor embeddings intuitively corroborate the document-level predictions, providing a nuanced and interpretable article frame representation.
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