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Register a new userNPVec1: Word Embeddings for Nepali - Construction and Evaluation
NPVEC1: Word Embeddings للنيبالية - البناء والتقييم
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كلمة تضمين خرائط الكلمات إلى ناقلات الأرقام الحقيقية.وهي مشتقة من كوربوس كبيرة ومن المعروف أنها تلتقط المعرفة الدلالية من الجثة.يعد Word Embedding مكونا حاسما للعديد من أساليب التعلم العميق الحديثة.ومع ذلك، فإن إنشاء Word Good Legeddings هو تحدي خاص لغات الموارد المنخفضة مثل النيبالية بسبب عدم توفر كوربوس نص كبير.في هذه الورقة، نقدم NPVEC1 والتي تتألف من 25 كلمة نيبالية من النيبالية التي اشتوعناها من كوربوس كبيرة باستخدام القفازات و Word2VEC و FastText و Bert.ونحن نقدم كذلك التقييمات الجوهرية والخارجية لهذه الأشرطة باستخدام مقاييس وأساليب راسخة.يتم تدريب هذه النماذج باستخدام الرموز 279 مليون كلمة وهي أكبر embeddings مدربة على الإطلاق للغة النيبالية.علاوة على ذلك، لقد جعلنا هذه الأشرطة المتاحة للجمهور لتسريع تطوير طلبات معالجة اللغة الطبيعية (NLP) في النيبالية.
Word Embedding maps words to vectors of real numbers. It is derived from a large corpus and is known to capture semantic knowledge from the corpus. Word Embedding is a critical component of many state-of-the-art Deep Learning techniques. However, generating good Word Embeddings is a special challenge for low-resource languages such as Nepali due to the unavailability of large text corpus. In this paper, we present NPVec1 which consists of 25 state-of-art Word Embeddings for Nepali that we have derived from a large corpus using Glove, Word2Vec, FastText, and BERT. We further provide intrinsic and extrinsic evaluations of these Embeddings using well established metrics and methods. These models are trained using 279 million word tokens and are the largest Embeddings ever trained for Nepali language. Furthermore, we have made these Embeddings publicly available to accelerate the development of Natural Language Processing (NLP) applications in Nepali.
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