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Register a new userKeyword Extraction Using Unsupervised Learning on the Document's Adjacency Matrix
استخراج الكلمات الرئيسية باستخدام التعلم غير المدعوم في مصفوفة المستند المجاورة
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يعيد هذا العمل أن المعلومات المقدمة من الرسم البياني للكلمات واستخدامها النموذجي من خلال نهج التصنيف المستندة إلى الرسم البياني في سياق استخراج الكلمات الرئيسية.عادة ما تستخدم الأساليب الرسمية القائمة على الرسم البياني المعروف عادة المعرفة من تمثيلات ناقلات Word خلال عملية الترتيب عبر تدابير مركزية شهيرة (على سبيل المثال، تصنيف الصفحات) دون إعطاء الدور الأساسي لتوزيع الناقلات.نحن نعتبر مصفوفة مجاورة تتوافق مع الرسم البياني لكلم وثيقة نصية مستهدفة كتمثيل متجه لمفرداته.نقترح النمذجة القائمة على التوزيع في هذه المصفوفة المجاورة باستخدام خوارزميات (التعلم) غير المعروضة.يتم تأكيد فعالية نهج النمذجة القائمة على التوزيع مقارنة بالأساليب الرسمية القائمة على الرسم البياني في الرسم البياني من خلال دراسة تجريبية واسعة النطاق وفقا لدرجة F1.رمزنا متاح على جيثب.
This work revisits the information given by the graph-of-words and its typical utilization through graph-based ranking approaches in the context of keyword extraction. Recent, well-known graph-based approaches typically employ the knowledge from word vector representations during the ranking process via popular centrality measures (e.g., PageRank) without giving the primary role to vectors' distribution. We consider the adjacency matrix that corresponds to the graph-of-words of a target text document as the vector representation of its vocabulary. We propose the distribution-based modeling of this adjacency matrix using unsupervised (learning) algorithms. The efficacy of the distribution-based modeling approaches compared to state-of-the-art graph-based methods is confirmed by an extensive experimental study according to the F1 score. Our code is available on GitHub.
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Keyword extraction is the task of identifying words (or multi-word expressions) that best describe a given document and serve in news portals to link articles of similar topics. In this work, we develop and evaluate our methods on four novel data set
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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 strengt
hs 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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