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Register a new userMulti-Label Classification of Chinese Humor Texts Using Hypergraph Attention Networks
التصنيف متعدد العلامات من النصوص الفكاهة الصينية باستخدام شبكات اهتمام Hypergraph
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نستخدم شبكات انتباه Hypergraph (Hypergat) للتعرف على ملصقات متعددة من النصوص الصينية الفكاهة.نحن أولا تمثل مزحة كملفوق.يتم استخدام هياكل Hyperedge المتسلسلة واللالسة الدلالية لبناء Hyperedges.ثم، يتم اعتماد آليات الاهتمام لمعلومات السياق التجميعية المضمنة في العقد والمواد التوظيف.أخيرا، نستخدم Hypergat المدربين لإكمال مهمة تصنيف العلامات المتعددة.أظهرت النتائج التجريبية على مجموعة بيانات الفكاهة الصينية متعددة التسمية أن نموذج Hypergat يتفوق على النماذج السابقة على التسلسل السابق (CNN، Bilstm، FastText) ورسم الرسم البياني (Graph-CNN، TextGcn، GNN Level Student).
We use Hypergraph Attention Networks (HyperGAT) to recognize multiple labels of Chinese humor texts. We firstly represent a joke as a hypergraph. The sequential hyperedge and semantic hyperedge structures are used to construct hyperedges. Then, attention mechanisms are adopted to aggregate context information embedded in nodes and hyperedges. Finally, we use trained HyperGAT to complete the multi-label classification task. Experimental results on the Chinese humor multi-label dataset showed that HyperGAT model outperforms previous sequence-based (CNN, BiLSTM, FastText) and graph-based (Graph-CNN, TextGCN, Text Level GNN) deep learning models.
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أصبحت قضية استرجاع المعلومات في يومنا هذا من أهم القضايا والتحدّيات التي تشغل العالم كنتيجة منطقية للتطوّر التكنولوجي المتسارع والتقدم الهائل في الفكر الإنساني والبحوث والدراسات العلمية في شتى فروع المعرفة وما رافقه من ازدياد في كميات المعلومات إلى ح
دّ يصعب التحكم بها والتعامل معها.
لذا نهدف في مشروعنا إلى تقديم نظام استرجاع معلومات يقوم بتصنيف المستندات حسب محتواها إلا أن عمليّة استرجاع المعلومات تحوي درجة من عدم التأكد في كل مرحلة من مراحلها لذا اعتمدنا على شبكات بيز للقيام بعملية التصنيف وهي شبكات احتماليّة تحوّل المعلومات إلى علاقات cause-and-effect و تعتبر واحدة من أهم الطرق الواعدة لمعالجة حالة عدم التأكد .
في البدء نقوم بالتعريف بأساسيّات شبكات بيز ونشرح مجموعة من خوارزميّات بنائها وخوارزميّات الاستدلال المستخدمة ( ولها نوعان دقيق وتقريبي).
يقوم هذه النظام بإجراء مجموعة من عمليّات المعالجة الأوليّة لنصوص المستندات ثم تطبيق عمليات إحصائية واحتمالية في مرحلة تدريب النظام والحصول على بنية شبكة بيز الموافقة لبيانات التدريب و يتم تصنيف مستند مدخل باستخدام مجموعة من خوارزميات الاستدلال الدقيق في شبكة بيز الناتجة لدينا.
بما أنّ أداء أي نظام استرجاع معلومات عادة ما يزداد دقّة عند استخدام العلاقات بين المفردات (terms) المتضمّنة في مجموعة مستندات فسنأخذ بعين الاعتبار نوعين من العلاقات في بناء الشبكة:
1- العلاقات بين المفردات(terms).
2- العلاقات بين المفردات والأصناف(classes).
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