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Register a new userBack to the Basics: A Quantitative Analysis of Statistical and Graph-Based Term Weighting Schemes for Keyword Extraction
العودة إلى الأساسيات: تحليل كمي لخطط ترجغ المصطلح الإحصائي والرسوم البياني لاستخراج الكلمات الرئيسية
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تستخدم مصطلح خطط الترجغ على نطاق واسع في معالجة اللغة الطبيعية واسترجاع المعلومات. على وجه الخصوص، فإن وزن المصطلح هو الأساس لاستخراج الكلمات الرئيسية. ومع ذلك، هناك عدد قليل نسبيا دراسات التقييم التي ألقت الضوء على نقاط القوة وأوجه القصور في كل مخطط للتوازن. في الواقع، في معظم الحالات، يلجأ الباحثون والممارسون في معظم الحالات إلى TF-IDF المعروفة بشكل افتراضي، على الرغم من وجود بدائل أخرى مناسبة، بما في ذلك النماذج القائمة على الرسم البياني. في هذه الورقة، نقوم بإجراء مقارنة تجريبية وشاملة واسعة النطاق من كل من أساليب الترجيح الإحصائية والرصاص القائمة على الرسم البياني في سياق استخراج الكلمات الرئيسية. يكشف تحليلنا عن بعض النتائج المثيرة للاهتمام مثل مزايا الخصوصية المعروفة الأقل شهرة فيما يتعلق ب TF-IDF، أو الاختلافات النوعية بين الأساليب الإحصائية والرصاص القائمة على الرسم البياني. وأخيرا، بناء على نتائجنا نناقشها واستنباد بعض الاقتراحات للممارسين. تعد شفرة المصدر لإعادة إنتاج نتائجنا التجريبية، بما في ذلك مكتبة استخراج الكلمات الرئيسية، متوفرة في المستودع التالي: https://github.com/asahi417/kex
Term weighting schemes are widely used in Natural Language Processing and Information Retrieval. In particular, term weighting is the basis for keyword extraction. However, there are relatively few evaluation studies that shed light about the strengths and shortcomings of each weighting scheme. In fact, in most cases researchers and practitioners resort to the well-known tf-idf as default, despite the existence of other suitable alternatives, including graph-based models. In this paper, we perform an exhaustive and large-scale empirical comparison of both statistical and graph-based term weighting methods in the context of keyword extraction. Our analysis reveals some interesting findings such as the advantages of the less-known lexical specificity with respect to tf-idf, or the qualitative differences between statistical and graph-based methods. Finally, based on our findings we discuss and devise some suggestions for practitioners. Source code to reproduce our experimental results, including a keyword extraction library, are available in the following repository: https://github.com/asahi417/kex
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