تناقش ورقة الاستقصاء / المركبة هذه الطرق لتحسين تغطية الموارد مثل WordNet.RAPP تقدر الارتباطات، RHO، بين إحصائيات كوربوس ومعايير الهاجولية.RHO يحسن مع الكمية (حجم كوربوس) والجودة (التوازن).1M الكلمات تكفي لتقديرات بسيطة (ترددات غير منغرام)، ولكن 100x على الأقل مطلوب لتقديرات جيدة للجمعيات والمواد المدمجة.نظرا مثل هذه التقديرات، فإن تغطية Wordnet رائعة.تم تطوير WordNET في SEMCOR، عينة صغيرة (كلمات 200K) من كوربوس البني.محاولات إكمال الرسم البياني المعرفي (KGC) تعلم الروابط المفقودة من مجموعات فرعية من مجموعات فرعية.لكن تقديرات Rapp للأحجام تشير إلى أنها ستكون أكثر ربحية لجمع المزيد من البيانات من استنتاج المعلومات المفقودة التي ليست موجودة.
This survey/position paper discusses ways to improve coverage of resources such as WordNet. Rapp estimated correlations, rho, between corpus statistics and pyscholinguistic norms. rho improves with quantity (corpus size) and quality (balance). 1M words is enough for simple estimates (unigram frequencies), but at least 100x more is required for good estimates of word associations and embeddings. Given such estimates, WordNet's coverage is remarkable. WordNet was developed on SemCor, a small sample (200k words) from the Brown Corpus. Knowledge Graph Completion (KGC) attempts to learn missing links from subsets. But Rapp's estimates of sizes suggest it would be more profitable to collect more data than to infer missing information that is not there.
References used
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