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Register a new userRare-Class Dialogue Act Tagging for Alzheimer's Disease Diagnosis
قانون حوار من الدرجة النادرة وضع علامات لتشخيص مرض الزهايمر
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يرتبط مرض الزهايمر (الإعلان) بالعديد من التغييرات المميزة، ليس فقط في لغة الفرد ولكن أيضا في أنماط تفاعلية لاحظت في الحوار. تميل التغييرات الأكثر إرشادية لهذا النوع الأخير إلى أن تكون مرتبطة بأعمال الحوار النادرة نسبيا (DAS)، مثل المشاركين في التبادلات والردود على أنواع معينة من الأسئلة. ومع ذلك، يركز معظم الأعمال الموجودة في العلامة في DA على تحسين الأداء المتوسط، وتحديد أولويات فئات أكثر تواترا؛ وبالتالي فإنه يعطي أداء ضعيفا على هذه الفصول الدراسية النادرة وليس مناسبا للتطبيق على تحليل الإعلانات. في هذه الورقة، نحقق في وضع علامات على وجه التحديد بالنسبة لفئة DAS النادرة، باستخدام نموذج Bilstm هرمي مع طرق مختلفة لإدماج المعلومات من الكلام السابق وعلامات التنمية في السياق. نظهر أن هذا يمكن أن يعطي أداء جيدا لفصول DA نادرة على كل من Corpus لوحة المفاتيح العامة (SWDA) ومجموعة بيانات محادثة محددة من الإعلانات، ومجموعة محادثة Carolinas (CCC)؛ وأن مخرجات Tagger ثم تساهم بمعلومات مفيدة لتمييز المرضى وبدون إعلان
Alzheimer's Disease (AD) is associated with many characteristic changes, not only in an individual's language but also in the interactive patterns observed in dialogue. The most indicative changes of this latter kind tend to be associated with relatively rare dialogue acts (DAs), such as those involved in clarification exchanges and responses to particular kinds of questions. However, most existing work in DA tagging focuses on improving average performance, effectively prioritizing more frequent classes; it thus gives a poor performance on these rarer classes and is not suited for application to AD analysis. In this paper, we investigate tagging specifically for rare class DAs, using a hierarchical BiLSTM model with various ways of incorporating information from previous utterances and DA tags in context. We show that this can give good performance for rare DA classes on both the general Switchboard corpus (SwDA) and an AD-specific conversational dataset, the Carolinas Conversation Collection (CCC); and that the tagger outputs then contribute useful information for distinguishing patients with and without AD
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