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Register a new userApples to Apples: A Systematic Evaluation of Topic Models
التفاح للتفاح: تقييم منهجي لنماذج الموضوع
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من النماذج الإحصائية إلى النماذج العصبية، تم اقتراح مجموعة واسعة من خوارزميات نمذجة الموضوعات في الأدب. ومع ذلك، بسبب تنوع مجموعات البيانات والمقاييس، لم تكن هناك العديد من الجهود لمقارنة أدائها بشكل منهجي على نفس المعايير وتحت نفس الشروط. في هذه الورقة، نقدم مجموعة مختارة من 9 تقنيات نمذجة موضوعا من حالة الفن التي تعكس تنوع مناهج المهمة، لمحة عامة عن المقاييس المختلفة المستخدمة لمقارنة أدائها، وتحديات إجراء هذه المقارنة. نحن نقيم تجريبيا أداء هذه النماذج على إعدادات مختلفة تعكس مجموعة متنوعة من الظروف الواقعية من حيث حجم مجموعة البيانات وعدد المواضيع وتوزيع الموضوعات، بعد عمليات المعالجة والتتقييم المتطابقة. باستخدام كل من المقاييس التي تعتمد على الخصائص الجوهرية لمجموعات البيانات (مقاييس الاتساق المختلفة)، بالإضافة إلى المعرفة الخارجية (تضييع Word Adgeddings وموضوع الحقيقة)، تكشف تجاربنا عدة أوجه القصور فيما يتعلق بالممارسات المشتركة في تقييم نماذج الموضوعات.
From statistical to neural models, a wide variety of topic modelling algorithms have been proposed in the literature. However, because of the diversity of datasets and metrics, there have not been many efforts to systematically compare their performance on the same benchmarks and under the same conditions. In this paper, we present a selection of 9 topic modelling techniques from the state of the art reflecting a diversity of approaches to the task, an overview of the different metrics used to compare their performance, and the challenges of conducting such a comparison. We empirically evaluate the performance of these models on different settings reflecting a variety of real-life conditions in terms of dataset size, number of topics, and distribution of topics, following identical preprocessing and evaluation processes. Using both metrics that rely on the intrinsic characteristics of the dataset (different coherence metrics), as well as external knowledge (word embeddings and ground-truth topic labels), our experiments reveal several shortcomings regarding the common practices in topic models evaluation.
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