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Register a new userAn Empirical Analysis of Topic Models: Uncovering the Relationships between Hyperparameters, Document Length and Performance Measures
تحليل تجريبي لنماذج الموضوع: يكشف عن العلاقات بين فرط الدم، وطول المستندات وتدابير الأداء
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نماذج الموضوع العصبي هي النماذج العصبية الأخيرة تهدف إلى استخراج الموضوعات الرئيسية من مجموعة من الوثائق.عادة ما تكون مقارنة هذه النماذج محدودة لأن فرط الدم محتجز ثابتة.في هذه الورقة، نقدم تحليلا تجريبي ومقارنة بين نماذج الموضوعات العصبية من خلال العثور على HyperParameters المثلى لكل نموذج لأربعة تدابير أداء مختلفة تبني تحسين بايزي هدف واحد.هذا يسمح لنا بتحديد متانة نموذج موضوع للعديد من مقاييس التقييم.كما أننا تظهر بشكل تجريبي تأثير طول الوثائق على مختلف المقاييس الأمثل واكتشف مقاييس التقييم الموجودة في صراع أو اتفاق مع بعضنا البعض.
Neural Topic Models are recent neural models that aim at extracting the main themes from a collection of documents. The comparison of these models is usually limited because the hyperparameters are held fixed. In this paper, we present an empirical analysis and comparison of Neural Topic Models by finding the optimal hyperparameters of each model for four different performance measures adopting a single-objective Bayesian optimization. This allows us to determine the robustness of a topic model for several evaluation metrics. We also empirically show the effect of the length of the documents on different optimized metrics and discover which evaluation metrics are in conflict or agreement with each other.
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