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Register a new userExtracting Topics with Simultaneous Word Co-occurrence and Semantic Correlation Graphs: Neural Topic Modeling for Short Texts
استخراج المواضيع مع الرسوم البيانية الوصية المتزامنة واللوانية الدلالية: النمذجة النمذجة العصبية للنصوص القصيرة
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أصبح نص قصير في الوقت الحاضر أشكالا أكثر عصرية من البيانات النصية، على سبيل المثال، منشورات Twitter، عناوين الأخبار ومراجعات المنتجات. يلعب استخراج الموضوعات الدلالية من النصوص القصيرة دورا مهما في مجموعة واسعة من تطبيقات NLP، ومصمم الموضوع العصبي الآن أداة رئيسية لتحقيقها. بدافع من تعلم موضوعات أكثر متماسكا ودلاليا، في هذه الورقة نطور نموذج موضوع عصبي رواية يدعى طراز موضوع الرسم البياني المزدوج Word (DWGTM)، والذي يستخرج الموضوعات من الرسوم البيانية المرتبطة بالكلمة المتزامنة وترابط العلاقة الدلالية. لتكون محددة، نتعلم ميزات كلمة من الرسم البياني العالمي للكلمة المشتركة، وذلك لاستيعاب معلومات حدوث كلمة غنية؛ ثم نقوم بإنشاء ميزات نصية مع ميزات Word، وإطعامها في شبكة تشفير للحصول على نسب موضوعية لكل نص؛ أخيرا، نعيد إعادة بناء الرسوم البيانية الرسمية والكلمات الرسم البياني مع التوزيعات الموضعية وميزات Word، على التوالي. بالإضافة إلى ذلك، لالتقاط دلالات الكلمات، نقوم أيضا بتطبيق ميزات Word لإعادة بناء الرسم البياني للعلاقة الدلالية كلمة محسوبة بواسطة Adgeddings المدربة للكلمة المدربة مسبقا. بناء على هذه الأفكار، نقوم بصياغة DWGTM في نموذج الترميز التلقائي وتدريبه بكفاءة مع روح الاستدلال التباين العصبي. تتحقق النتائج التجريبية التي يمكنها توليد DWGTM موضوعات أكثر متماسكة من النماذج الأساسية من طرازات موضوع الأساس.
Short text nowadays has become a more fashionable form of text data, e.g., Twitter posts, news titles, and product reviews. Extracting semantic topics from short texts plays a significant role in a wide spectrum of NLP applications, and neural topic modeling is now a major tool to achieve it. Motivated by learning more coherent and semantic topics, in this paper we develop a novel neural topic model named Dual Word Graph Topic Model (DWGTM), which extracts topics from simultaneous word co-occurrence and semantic correlation graphs. To be specific, we learn word features from the global word co-occurrence graph, so as to ingest rich word co-occurrence information; we then generate text features with word features, and feed them into an encoder network to get topic proportions per-text; finally, we reconstruct texts and word co-occurrence graph with topical distributions and word features, respectively. Besides, to capture semantics of words, we also apply word features to reconstruct a word semantic correlation graph computed by pre-trained word embeddings. Upon those ideas, we formulate DWGTM in an auto-encoding paradigm and efficiently train it with the spirit of neural variational inference. Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models.
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