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Register a new userMulti-source Neural Topic Modeling in Multi-view Embedding Spaces
نمذجة موضوع عصبي متعدد المصدر في مساحات تضمين متعددة الرؤية
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على الرغم من أن Word Adgeddings والمواضيع هي تمثيل تكميلي، إلا أن العديد من الأعمال السابقة استخدمت فقط Arestrained Word Areging في النمذجة الموضوعية (العصبية) لمعالجة Sparsity البيانات في نص قصير أو مجموعة صغيرة من المستندات. يعرض هذا العمل إطارا للنمذجة النمذجة العصبية الرواية باستخدام مساحات تضمين متعددة الرؤية: (1) - Arbrained Topic-Embeddings، و (2) - Ardrained Word-Argeddings (غير حساس للسياق من القفازات والسياق الحساسة من نماذج بيرت) بالاشتراك من واحد أو العديد من المصادر لتحسين جودة الموضوع والتعامل بشكل أفضل مع Polysemy. عند القيام بذلك، نقوم أولا بإنشاء حمامات متعصفة من الموضوع المسبق (I.E.، TopicPool) و Adgeddings Word (I.E.، WordPool). بعد ذلك، حددنا واحدا أو أكثر من المجال (المجال) المصدر (SOB) ونقل المعرفة لتوجيه التعلم الهادف في المجال المستهدف Sparse. ضمن النمذجة الموضوعية العصبية، نحدد جودة المواضيع وتمثيلات المستند عبر التعميم (الحيرة)، إمكانية الترجمة الترجمة الترجمة الشفوية (تماسك الموضوع) واسترجاع المعلومات (IR) باستخدام مجموعات مستندات قصيرة ونص وطويلة وصغيرة من الأخبار والمجالات الطبية وبعد تقديم مساحات تضمين متعددة المشتريات متعددة المصدر، وقد أظهرنا نمذجة موضوع عصبي للحالة باستخدام 6 مصدر (الموارد العالية) و 5 أهداف (الموارد المنخفضة).
Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity in short-text or small collection of documents. This work presents a novel neural topic modeling framework using multi-view embed ding spaces: (1) pretrained topic-embeddings, and (2) pretrained word-embeddings (context-insensitive from Glove and context-sensitive from BERT models) jointly from one or many sources to improve topic quality and better deal with polysemy. In doing so, we first build respective pools of pretrained topic (i.e., TopicPool) and word embeddings (i.e., WordPool). We then identify one or more relevant source domain(s) and transfer knowledge to guide meaningful learning in the sparse target domain. Within neural topic modeling, we quantify the quality of topics and document representations via generalization (perplexity), interpretability (topic coherence) and information retrieval (IR) using short-text, long-text, small and large document collections from news and medical domains. Introducing the multi-source multi-view embedding spaces, we have shown state-of-the-art neural topic modeling using 6 source (high-resource) and 5 target (low-resource) corpora.
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