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Register a new userUnsupervised Keyphrase Extraction by Jointly Modeling Local and Global Context
استخراج المفاتيح غير المنسق من خلال النمذجة بشكل مشترك السياق المحلي والعالمي
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تستخدم الأساليب القائمة على نطاق واسع على نطاق واسع لمهام استخراج مفاتيح المفاتيح غير المنشأة (UKE). بشكل عام، تقوم هذه الأساليب ببساطة بحساب أوجه التشابه بين Aregeddings و Award Action، وهو غير كاف لالتقاط سياق مختلف لنموذج UKE أكثر فعالية. في هذه الورقة، نقترح طريقة جديدة ل UKE، حيث يتم تصميم السياقات المحلية والعالمية بشكل مشترك. من وجهة نظر عالمية، نقوم بحساب التشابه بين عبارة معينة والوثيقة بأكملها في مساحة المتجهة كما نماذج تضمينها الانتقالية. من حيث الرأي المحلي، نقوم أولا ببناء هيكل رسم بياني يستند إلى المستند حيث تعتبر العبارات كأعلى رؤوس والحواف هي أوجه التشابه بين القمم. بعد ذلك، اقترحنا طريقة حساب مركزية جديدة لالتقاط المعلومات البارزة المحلية بناء على هيكل الرسم البياني. أخيرا، نكتف على نمذجة السياق العالمي والمحلي للتصنيف. نقوم بتقييم نماذجنا على ثلاثة معايير عامة (Inspec، DUC 2001، Semeval 2010) ومقارنتها مع النماذج الموجودة في أحدث النماذج. تظهر النتائج أن نموذجنا يفوق معظم النماذج أثناء التعميم بشكل أفضل على مستندات المدخلات ذات النطاقات والطول المختلفة. تظهر دراسة الاجتثاث الإضافية أن كل من المعلومات المحلية والعالمية أمر بالغ الأهمية لمهام استخراج المفاتيح غير المنشورة.
Embedding based methods are widely used for unsupervised keyphrase extraction (UKE) tasks. Generally, these methods simply calculate similarities between phrase embeddings and document embedding, which is insufficient to capture different context for a more effective UKE model. In this paper, we propose a novel method for UKE, where local and global contexts are jointly modeled. From a global view, we calculate the similarity between a certain phrase and the whole document in the vector space as transitional embedding based models do. In terms of the local view, we first build a graph structure based on the document where phrases are regarded as vertices and the edges are similarities between vertices. Then, we proposed a new centrality computation method to capture local salient information based on the graph structure. Finally, we further combine the modeling of global and local context for ranking. We evaluate our models on three public benchmarks (Inspec, DUC 2001, SemEval 2010) and compare with existing state-of-the-art models. The results show that our model outperforms most models while generalizing better on input documents with different domains and length. Additional ablation study shows that both the local and global information is crucial for unsupervised keyphrase extraction tasks.
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Keyword or keyphrase extraction is to identify words or phrases presenting the main topics of a document. This paper proposes the AttentionRank, a hybrid attention model, to identify keyphrases from a document in an unsupervised manner. AttentionRank
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