Üstverilerin Derin Öğrenme Algoritmaları Kullanılarak Otomatik Olarak Çıkartılması ve Sınıflanması

Günümüzde bilişim teknolojilerinin yaygınlaşması sebebiyle dijital içerik ihtiyacı artmıştır. Bu içeriklerin oluşturulması zaman alıcı ve maliyetli bir süreçtir. İçerik oluşturulurken öğrenme nesnelerinden faydalanılmaktadır. Bu nesnelerin bilgisayarlar tarafından keşfedilebilir ve okunabilir olması yeniden kullanılabilirlik ve paylaşılabilirlik açısından önemlidir. Bu sebeple nesneler tanımlayıcı kimlik bilgilerini içeren üstveriler ile bütünleşik olarak kullanılmaktadırlar. Bu üstveriler ne kadar düzgün oluşturulup sınıflandırılırsa nesnelerin kullanılabilirliği o derece artmış olmaktadır. Bu sebeple nesnelerden otomatik üstveri çıkartan birçok yöntem geliştirilmiştir. Bu çalışmada da Konvolüsyonel Sinir Ağları (KSA), Tekrarlayan Sinir Ağları (TSA) gibi derin öğrenme ve Doğal Dil İşleme (DDİ) yöntemleri kullanılarak öğrenme nesnelerindeki içeriklerden otomatik olarak üstveri çıkartılması ve sınıflaması yapılmıştır. Sistemin başarısı ve doğruluğu örnek öğrenme nesneleri ile test edilmiştir. Sonuçlar sistemin başarılı bir şekilde kullanılabileceğini göstermiştir.

Anahtar Kelimeler:

Üstveri çıkartma, Konvolüsyonel sinir ağları, Tekrarlayan sinir ağları

Automatic Metadata Extraction and Classification by using Deep Learning Algorithms

The need for digital content has increased due to the widespread use of information technologies today. Creating these contents is a time consuming and costly process. Learning objects are used while creating the content. It is important that these objects can be discovered and readable by computers in terms of reusability and shareability. For this reason, objects are used in integration with metadata containing identifying information. The more properly these metadata are created and classified, the greater the usability of the objects. For this reason, many methods have been developed that automatically extract metadata from objects. In this study, metadata extraction and classification from the contents of learning objects were made automatically by using deep learning methods such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Natural Language Processing (NLP). The success and accuracy of the system has been tested with sample learning objects. The results showed that the system can be used successfully.

Keywords:

Metadata extraction, Convolutional neural networks, Recurrent neural networks,

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