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Register a new userExtending Neural Keyword Extraction with TF-IDF tagset matching
تمديد استخراج الكلمات الرئيسية العصبية مع TF-IDF Tagset مطابقة
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استخراج الكلمات الرئيسية هي مهمة تحديد الكلمات (أو تعبيرات متعددة الكلمة) التي تصف أفضل وثيقة معينة وخدمة في بوابات الأخبار لربط مقالات من مواضيع مماثلة. في هذا العمل، نطور وتقييم أساليبنا على أربع مجموعات بيانات جديدة تغطي لغات أقل ممثلة تمثيلا، لغات غنية بالمظورة في صناعة وسائل الإعلام الإخبارية الأوروبية (الكرواتية، الإستونية، اللاتفية والروسية). أولا، نؤدي تقييم اثنين من أساليب المحولات العصبية الخاضعة للإشراف، والتكلمة العصبية القائمة على المحولات لتحديد الكلمات الرئيسية (TNT-KID) وتمثيل التشفير ثنائي الاتجاه من المحولات (بيرت) مع وجود ذاكرة عشوائية قصيرة الأجل طويلة الأجل الطويلة الأجل (BILSTM) رأس التصنيف CRF)، ومقارنتها بموجب تردد خط الأساس - تعتمد على تردد المستندات (TF-IDF) مقرها. بعد ذلك، نظهر أنه من خلال الجمع بين الكلمات الرئيسية التي تم استرجها من قبل كل من الأساليب القائم على المحولات العصبية وتوسيع المجموعة النهائية من الكلمات الرئيسية ذات التقنية القائمة على TF-IDF غير المدعومة، يمكننا تحسين استدعاء النظام بشكل كبير، مما يجعلها مناسبة للاستخدام ك نظام التوصية في بيئة مجلس الإعلام.
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 sets covering less-represented, morphologically-rich languages in European news media industry (Croatian, Estonian, Latvian, and Russian). First, we perform evaluation of two supervised neural transformer-based methods, Transformer-based Neural Tagger for Keyword Identification (TNT-KID) and Bidirectional Encoder Representations from Transformers (BERT) with an additional Bidirectional Long Short-Term Memory Conditional Random Fields (BiLSTM CRF) classification head, and compare them to a baseline Term Frequency - Inverse Document Frequency (TF-IDF) based unsupervised approach. Next, we show that by combining the keywords retrieved by both neural transformer-based methods and extending the final set of keywords with an unsupervised TF-IDF based technique, we can drastically improve the recall of the system, making it appropriate for usage as a recommendation system in the media house environment.
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