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Register a new userHierarchical Multi-label Text Classification with Horizontal and Vertical Category Correlations
تصنيف النص الهرمي متعدد الملصقات مع ارتباطات الفئات الأفقية والرأسية
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يتعامل تصنيف النص المتعدد التسميات الهرمية (HMTC) مع المهمة الصعبة التي يمكن فيها تعيين مثيل للفئات المهيكية المتعددة في نفس الوقت. غالبية الدراسات السابقة إما أن تركز على تقليل مهمة HMTC إلى مشكلة مسطحة متعددة العلامات تتجاهل علاقات الفئات الرأسية أو استغلال التبعيات عبر مستويات هرمية مختلفة دون النظر في الارتباط الأفقي بين الفئات في نفس المستوى، والتي تؤدي حتما إلى المعلومات الأساسية خسارة. في هذه الورقة، نقترح إطار رواية HMTC يرى الارتباطات الفئة الرأسية والأفقية. على وجه التحديد، نقوم أولا بتصميم شبكة عصبية متقطعة متوترة فضفاضة كمستغل في التمثيل للحصول على تمثيلات للكلمات والوثائق، والأهم من ذلك، والأهم من ذلك، والتعويضات الحكيمة للمستوى بالنسبة للفئات، والتي لا تعتبر في الأعمال السابقة. بعد ذلك، يتم اعتماد تمثيلات الفئة المستفادة لالتقاط التبعيات الرأسي بين مستويات التسلسل الهرمي للفئة ونموذج الارتباط الأفقي. أخيرا، استنادا إلى Admeddings و Emgeddings، نقوم بتصميم خوارزمية هجينة للتنبؤ بفئات الهيكل الهرمي بأكمله. تجارب واسعة أجريت على مجموعات بيانات HMTC العالمية الحقيقية صحة فعالية الإطار المقترح بدعوات كبيرة على خط الأساس.
Hierarchical multi-label text classification (HMTC) deals with the challenging task where an instance can be assigned to multiple hierarchically structured categories at the same time. The majority of prior studies either focus on reducing the HMTC task into a flat multi-label problem ignoring the vertical category correlations or exploiting the dependencies across different hierarchical levels without considering the horizontal correlations among categories at the same level, which inevitably leads to fundamental information loss. In this paper, we propose a novel HMTC framework that considers both vertical and horizontal category correlations. Specifically, we first design a loosely coupled graph convolutional neural network as the representation extractor to obtain representations for words, documents, and, more importantly, level-wise representations for categories, which are not considered in previous works. Then, the learned category representations are adopted to capture the vertical dependencies among levels of category hierarchy and model the horizontal correlations. Finally, based on the document embeddings and category embeddings, we design a hybrid algorithm to predict the categories of the entire hierarchical structure. Extensive experiments conducted on real-world HMTC datasets validate the effectiveness of the proposed framework with significant improvements over the baselines.
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