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Register a new userNeural Attention-Aware Hierarchical Topic Model
نموذج موضوع الاهتمام العصبي
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نماذج الموضوعات العصبية (NTMS) تطبيق الشبكات العصبية العميقة إلى نمذجة الموضوعات. على الرغم من نجاحها، تجاهل NTMS عموما جائبا مهمين: (1) فقط يتم استخدام معلومات عدد الكلمات على مستوى المستند للتدريب، في حين يتم تجاهل المزيد من المعلومات ذات المستوى الجميل على مستوى الجملة، و (2) المعرفة الدلالية الخارجية فيما يتعلق بالوثائق، الجمل والكلمات لم يتم استغلالها للتدريب. لمعالجة هذه المشكلات، نقترح نموذج NTM (VAE) AutoNCoder (VAE) بشكل مشترك إعادة بناء الجملة وكلمة الوثيقة التي تهمها مجموعات من المبيعات الموضعية في كيس الكلمات (القوس) و EMBEDDINGS الدلالي المدرب مسبقا. يتم تحويل المدينات المدربة مسبقا لأول مرة إلى مساحة موضة كامنة مشتركة لمواءمة دلالاتها مع تضمين القوس. يتميز نموذجنا أيضا باختلاف KL هرمي للاستفادة من تضمينات كل وثيقة لتوسيع نطاق جملهم، مما يدفع المزيد من الاهتمام للجمل ذات الصلة الدولى. أظهرت كل من التجارب الكمية والنوعية فعالية نموذجنا في 1) خفض أخطاء إعادة الإعمار على كل من المستويات الجملة والوثائق، و 2) اكتشاف موضوعات أكثر تماسكا من مجموعات بيانات العالم الحقيقي.
Neural topic models (NTMs) apply deep neural networks to topic modelling. Despite their success, NTMs generally ignore two important aspects: (1) only document-level word count information is utilized for the training, while more fine-grained sentence-level information is ignored, and (2) external semantic knowledge regarding documents, sentences and words are not exploited for the training. To address these issues, we propose a variational autoencoder (VAE) NTM model that jointly reconstructs the sentence and document word counts using combinations of bag-of-words (BoW) topical embeddings and pre-trained semantic embeddings. The pre-trained embeddings are first transformed into a common latent topical space to align their semantics with the BoW embeddings. Our model also features hierarchical KL divergence to leverage embeddings of each document to regularize those of their sentences, paying more attention to semantically relevant sentences. Both quantitative and qualitative experiments have shown the efficacy of our model in 1) lowering the reconstruction errors at both the sentence and document levels, and 2) discovering more coherent topics from real-world datasets.
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